code: def convert_uvit_to_diffusers(ckpt, diffusers_model):
      """
      Converts a UniDiffuser uvit_v*.pth checkpoint to a diffusers UniDiffusersModel.
      """
      # uvit_v*.pth ckpt is a torch state dict
      uvit_state_dict = torch.load(ckpt, map_location="cpu")
  
      new_state_dict = {}
  
      # Input layers
      new_state_dict["vae_img_in.proj.weight"] = uvit_state_dict[
          "patch_embed.proj.weight"
      ]
      new_state_dict["vae_img_in.proj.bias"] = uvit_state_dict[
          "patch_embed.proj.bias"
      ]
      new_state_dict["clip_img_in.weight"] = uvit_state_dict[
          "clip_img_embed.weight"
      ]
      new_state_dict["clip_img_in.bias"] = uvit_state_dict[
          "clip_img_embed.bias"
      ]
      new_state_dict["text_in.weight"] = uvit_state_dict[
          "text_embed.weight"
      ]
      new_state_dict["text_in.bias"] = uvit_state_dict[
          "text_embed.bias"
      ]
  
      new_state_dict["pos_embed"] = uvit_state_dict["pos_embed"]
  
      # Handle data type token embeddings for UniDiffuser-v1
      if (
          "token_embedding.weight" in uvit_state_dict
          and diffusers_model.use_data_type_embedding
      ):
          new_state_dict["data_type_pos_embed_token"] = uvit_state_dict[
              "pos_embed_token"
          ]
          new_state_dict[
              "data_type_token_embedding.weight"
          ] = uvit_state_dict["token_embedding.weight"]
  
      # Also initialize the PatchEmbedding in UTransformer2DModel with the PatchEmbedding from the checkpoint.
      # This isn't used in the current implementation, so might want to remove.
      new_state_dict[
          "transformer.pos_embed.proj.weight"
      ] = uvit_state_dict["patch_embed.proj.weight"]
      new_state_dict[
          "transformer.pos_embed.proj.bias"
      ] = uvit_state_dict["patch_embed.proj.bias"]
  
      # Output layers
      new_state_dict["transformer.norm_out.weight"] = uvit_state_dict[
          "norm.weight"
      ]
      new_state_dict["transformer.norm_out.bias"] = uvit_state_dict[
          "norm.bias"
      ]
  
      new_state_dict["vae_img_out.weight"] = uvit_state_dict[
          "decoder_pred.weight"
      ]
      new_state_dict["vae_img_out.bias"] = uvit_state_dict[
          "decoder_pred.bias"
      ]
      new_state_dict["clip_img_out.weight"] = uvit_state_dict[
          "clip_img_out.weight"
      ]
      new_state_dict["clip_img_out.bias"] = uvit_state_dict[
          "clip_img_out.bias"
      ]
      new_state_dict["text_out.weight"] = uvit_state_dict[
          "text_out.weight"
      ]
      new_state_dict["text_out.bias"] = uvit_state_dict["text_out.bias"]
  
      # in_blocks
      in_blocks_prefixes = {
          ".".join(layer.split(".")[:2])
          for layer in uvit_state_dict
          if "in_blocks" in layer
      }
      for in_block_prefix in list(in_blocks_prefixes):
          convert_uvit_block_to_diffusers_block(
              uvit_state_dict, new_state_dict, in_block_prefix
          )
  
      # mid_block
      # Assume there's only one mid block
      convert_uvit_block_to_diffusers_block(
          uvit_state_dict, new_state_dict, "mid_block"
      )
  
      # out_blocks
      out_blocks_prefixes = {
          ".".join(layer.split(".")[:2])
          for layer in uvit_state_dict
          if "out_blocks" in layer
      }
      for out_block_prefix in list(out_blocks_prefixes):
          convert_uvit_block_to_diffusers_block(
              uvit_state_dict,
              new_state_dict,
              out_block_prefix,
              skip_connection=True,
          )
  
      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 17 for calling a global function with example usage: convert_uvit_to_diffusers(ckpt, diffusers_model) and returns: diffusers_model