code: def convert_caption_decoder_to_diffusers(ckpt, diffusers_model):
      """
      Converts a UniDiffuser caption_decoder.pth checkpoint to a diffusers UniDiffuserTextDecoder.
      """
      # caption_decoder.pth ckpt is a torch state dict
      checkpoint_state_dict = torch.load(ckpt, map_location="cpu")
      decoder_state_dict = {}
      # Remove the "module." prefix, if necessary
      caption_decoder_key = "module."
      for key in checkpoint_state_dict:
          if key.startswith(caption_decoder_key):
              decoder_state_dict[
                  key.replace(caption_decoder_key, "")
              ] = checkpoint_state_dict.get(key)
          else:
              decoder_state_dict[key] = checkpoint_state_dict.get(key)
  
      new_state_dict = {}
  
      # Encoder and Decoder
      new_state_dict["encode_prefix.weight"] = decoder_state_dict[
          "encode_prefix.weight"
      ]
      new_state_dict["encode_prefix.bias"] = decoder_state_dict[
          "encode_prefix.bias"
      ]
      new_state_dict["decode_prefix.weight"] = decoder_state_dict[
          "decode_prefix.weight"
      ]
      new_state_dict["decode_prefix.bias"] = decoder_state_dict[
          "decode_prefix.bias"
      ]
  
      # Internal GPT2LMHeadModel transformer model
      for key, val in decoder_state_dict.items():
          if key.startswith("gpt"):
              suffix = key[len("gpt") :]
              new_state_dict["transformer" + suffix] = val
  
      missing_keys, unexpected_keys = diffusers_model.load_state_dict(
          new_state_dict
      )
      for missing_key in missing_keys:
          print(f"Missing key: {missing_key}")
      for unexpected_key in unexpected_keys:
          print(f"Unexpected key: {unexpected_key}")
  
      return diffusers_model
  
imports: import argparse
  from argparse import Namespace
  import torch
  from transformers import CLIPImageProcessor, CLIPTextConfig, CLIPTextModel, CLIPTokenizer, CLIPVisionConfig, CLIPVisionModelWithProjection, GPT2Tokenizer
  from diffusers import AutoencoderKL, DPMSolverMultistepScheduler, UniDiffuserModel, UniDiffuserPipeline, UniDiffuserTextDecoder
label_desc: Use image node_id 18 for calling a global function with example usage: convert_caption_decoder_to_diffusers(ckpt, diffusers_model) and returns: diffusers_model