code: def unet_upblock_to_diffusers_checkpoint(
      model,
      checkpoint,
      *,
      diffusers_up_block_idx,
      original_up_block_idx,
      original_unet_prefix,
      num_head_channels,
  ):
      diffusers_checkpoint = {}
  
      diffusers_resnet_prefix = (
          f"up_blocks.{diffusers_up_block_idx}.resnets"
      )
      original_up_block_prefix = f"{original_unet_prefix}.output_blocks"
  
      up_block = model.up_blocks[diffusers_up_block_idx]
  
      num_resnets = len(up_block.resnets)
  
      if up_block.upsamplers is None:
          upsampler = False
      else:
          assert len(up_block.upsamplers) == 1
          upsampler = True
          # The upsample block is also a resnet
          num_resnets += 1
  
      has_attentions = hasattr(up_block, "attentions")
  
      for resnet_idx_inc in range(num_resnets):
          if upsampler and resnet_idx_inc == num_resnets - 1:
              # this is an upsample block
              if has_attentions:
                  # There is a middle attention block that we skip
                  original_resnet_block_idx = 2
              else:
                  original_resnet_block_idx = 1
  
              # we add the `minus 1` because the last two resnets are stuck together in the same output block
              full_resnet_prefix = f"{original_up_block_prefix}.{original_up_block_idx + resnet_idx_inc - 1}.{original_resnet_block_idx}"
  
              full_diffusers_resnet_prefix = (
                  f"up_blocks.{diffusers_up_block_idx}.upsamplers.0"
              )
          else:
              # this is a regular resnet block
              full_resnet_prefix = f"{original_up_block_prefix}.{original_up_block_idx + resnet_idx_inc}.0"
              full_diffusers_resnet_prefix = (
                  f"{diffusers_resnet_prefix}.{resnet_idx_inc}"
              )
  
          diffusers_checkpoint.update(
              resnet_to_diffusers_checkpoint(
                  checkpoint,
                  resnet_prefix=full_resnet_prefix,
                  diffusers_resnet_prefix=full_diffusers_resnet_prefix,
              )
          )
  
      if has_attentions:
          num_attentions = len(up_block.attentions)
          diffusers_attention_prefix = (
              f"up_blocks.{diffusers_up_block_idx}.attentions"
          )
  
          for attention_idx_inc in range(num_attentions):
              full_attention_prefix = f"{original_up_block_prefix}.{original_up_block_idx + attention_idx_inc}.1"
              full_diffusers_attention_prefix = (
                  f"{diffusers_attention_prefix}.{attention_idx_inc}"
              )
  
              diffusers_checkpoint.update(
                  attention_to_diffusers_checkpoint(
                      checkpoint,
                      attention_prefix=full_attention_prefix,
                      diffusers_attention_prefix=full_diffusers_attention_prefix,
                      num_head_channels=num_head_channels,
                  )
              )
  
      num_original_down_blocks = (
          num_resnets - 1 if upsampler else num_resnets
      )
  
      return diffusers_checkpoint, num_original_down_blocks
  
imports: import argparse
  import tempfile
  import torch
  from accelerate import load_checkpoint_and_dispatch
  from transformers import CLIPTextModelWithProjection, CLIPTokenizer
  from diffusers import UnCLIPPipeline, UNet2DConditionModel, UNet2DModel
  from diffusers.models.prior_transformer import PriorTransformer
  from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
  from diffusers.schedulers.scheduling_unclip import UnCLIPScheduler
label_desc: Use image node_id 19 for calling a global function with example usage: unet_upblock_to_diffusers_checkpoint(model, checkpoint) and returns: diffusers_checkpoint,  num_original_down_blocks