code: def convert_state_dict(unet_state_dict):
      """
      Convert the state dict of a U-Net model to match the key format expected by Kandinsky3UNet model.
      Args:
          unet_model (torch.nn.Module): The original U-Net model.
          unet_kandi3_model (torch.nn.Module): The Kandinsky3UNet model to match keys with.
  
      Returns:
          OrderedDict: The converted state dictionary.
      """
      # Example of renaming logic (this will vary based on your model's architecture)
      converted_state_dict = {}
      for key in unet_state_dict:
          new_key = key
          for pattern, new_pattern in MAPPING.items():
              new_key = new_key.replace(pattern, new_pattern)
  
          for dyn_pattern, dyn_new_pattern in DYNAMIC_MAP.items():
              has_matched = False
              if (
                  fnmatch.fnmatch(new_key, f"*.{dyn_pattern}.*")
                  and not has_matched
              ):
                  star = int(
                      new_key.split(dyn_pattern.split(".")[0])[
                          -1
                      ].split(".")[1]
                  )
  
                  if isinstance(dyn_new_pattern, tuple):
                      new_star = star + dyn_new_pattern[-1]
                      dyn_new_pattern = dyn_new_pattern[0]
                  else:
                      new_star = star
  
                  pattern = dyn_pattern.replace("*", str(star))
                  new_pattern = dyn_new_pattern.replace(
                      "*", str(new_star)
                  )
  
                  new_key = new_key.replace(pattern, new_pattern)
                  has_matched = True
  
          converted_state_dict[new_key] = unet_state_dict[key]
  
      return converted_state_dict
  
imports: import argparse
  import fnmatch
  from safetensors.torch import load_file
  from diffusers import Kandinsky3UNet
label_desc: Use image node_id 1 for calling a global function with example usage: convert_state_dict(unet_state_dict) and returns: converted_state_dict