code: def block_to_diffusers_checkpoint(
      block, checkpoint, block_idx, block_type
  ):
      block_prefix = (
          "inner_model.u_net.u_blocks"
          if block_type == "up"
          else "inner_model.u_net.d_blocks"
      )
      block_prefix = f"{block_prefix}.{block_idx}"
  
      diffusers_checkpoint = {}
  
      if not hasattr(block, "attentions"):
          n = 1  # resnet only
      elif not block.attentions[0].add_self_attention:
          n = 2  # resnet -> cross-attention
      else:
          n = 3  # resnet -> self-attention -> cross-attention)
  
      for resnet_idx, resnet in enumerate(block.resnets):
          # diffusers_resnet_prefix = f"{diffusers_up_block_prefix}.resnets.{resnet_idx}"
          diffusers_resnet_prefix = (
              f"{block_type}_blocks.{block_idx}.resnets.{resnet_idx}"
          )
          idx = (
              n * resnet_idx
              if block_type == "up"
              else n * resnet_idx + 1
          )
          resnet_prefix = (
              f"{block_prefix}.{idx}"
              if block_type == "up"
              else f"{block_prefix}.{idx}"
          )
  
          diffusers_checkpoint.update(
              resnet_to_diffusers_checkpoint(
                  resnet,
                  checkpoint,
                  diffusers_resnet_prefix=diffusers_resnet_prefix,
                  resnet_prefix=resnet_prefix,
              )
          )
  
      if hasattr(block, "attentions"):
          for attention_idx, attention in enumerate(block.attentions):
              diffusers_attention_prefix = f"{block_type}_blocks.{block_idx}.attentions.{attention_idx}"
              idx = (
                  n * attention_idx + 1
                  if block_type == "up"
                  else n * attention_idx + 2
              )
              self_attention_prefix = f"{block_prefix}.{idx}"
              cross_attention_prefix = f"{block_prefix}.{idx }"
              cross_attention_index = (
                  1 if not attention.add_self_attention else 2
              )
              idx = (
                  n * attention_idx + cross_attention_index
                  if block_type == "up"
                  else n * attention_idx + cross_attention_index + 1
              )
              cross_attention_prefix = f"{block_prefix}.{idx }"
  
              diffusers_checkpoint.update(
                  cross_attn_to_diffusers_checkpoint(
                      checkpoint,
                      diffusers_attention_prefix=diffusers_attention_prefix,
                      diffusers_attention_index=2,
                      attention_prefix=cross_attention_prefix,
                  )
              )
  
              if attention.add_self_attention is True:
                  diffusers_checkpoint.update(
                      self_attn_to_diffusers_checkpoint(
                          checkpoint,
                          diffusers_attention_prefix=diffusers_attention_prefix,
                          attention_prefix=self_attention_prefix,
                      )
                  )
  
      return diffusers_checkpoint
  
file_path: llmga/llmga/diffusers/scripts/convert_k_upscaler_to_diffusers.py
imports: import argparse
  import huggingface_hub
  import k_diffusion
  import torch
  from diffusers import UNet2DConditionModel
label_desc: Use image node_id 4 for calling a global function with example usage: block_to_diffusers_checkpoint(block, checkpoint, block_idx, block_type) and returns: diffusers_checkpoint