code: def unet_midblock_to_diffusers_checkpoint(
      model, checkpoint, *, num_head_channels
  ):
      diffusers_checkpoint = {}
  
      # block 0
  
      original_block_idx = 0
  
      diffusers_checkpoint.update(
          resnet_to_diffusers_checkpoint(
              checkpoint,
              diffusers_resnet_prefix="mid_block.resnets.0",
              resnet_prefix=f"middle_block.{original_block_idx}",
          )
      )
  
      original_block_idx += 1
  
      # optional block 1
  
      if (
          hasattr(model.mid_block, "attentions")
          and model.mid_block.attentions[0] is not None
      ):
          diffusers_checkpoint.update(
              attention_to_diffusers_checkpoint(
                  checkpoint,
                  diffusers_attention_prefix="mid_block.attentions.0",
                  attention_prefix=f"middle_block.{original_block_idx}",
                  num_head_channels=num_head_channels,
              )
          )
          original_block_idx += 1
  
      # block 1 or block 2
  
      diffusers_checkpoint.update(
          resnet_to_diffusers_checkpoint(
              checkpoint,
              diffusers_resnet_prefix="mid_block.resnets.1",
              resnet_prefix=f"middle_block.{original_block_idx}",
          )
      )
  
      return diffusers_checkpoint
  
imports: import argparse
  import os
  import tempfile
  import torch
  from accelerate import load_checkpoint_and_dispatch
  from diffusers import UNet2DConditionModel
  from diffusers.models.prior_transformer import PriorTransformer
  from diffusers.models.vq_model import VQModel
label_desc: Use image node_id 16 for calling a global function with example usage: unet_midblock_to_diffusers_checkpoint(model, checkpoint) and returns: diffusers_checkpoint