code: def vqvae_decoder_to_diffusers_checkpoint(model, checkpoint):
      diffusers_checkpoint = {}
  
      # conv in
      diffusers_checkpoint.update(
          {
              "decoder.conv_in.weight": checkpoint[
                  "decoder.conv_in.weight"
              ],
              "decoder.conv_in.bias": checkpoint[
                  "decoder.conv_in.bias"
              ],
          }
      )
  
      # up_blocks
  
      for diffusers_up_block_idx, up_block in enumerate(
          model.decoder.up_blocks
      ):
          # up_blocks are stored in reverse order in the VQ-diffusion checkpoint
          orig_up_block_idx = (
              len(model.decoder.up_blocks) - 1 - diffusers_up_block_idx
          )
  
          diffusers_up_block_prefix = (
              f"decoder.up_blocks.{diffusers_up_block_idx}"
          )
          up_block_prefix = f"decoder.up.{orig_up_block_idx}"
  
          # resnets
          for resnet_idx, resnet in enumerate(up_block.resnets):
              diffusers_resnet_prefix = (
                  f"{diffusers_up_block_prefix}.resnets.{resnet_idx}"
              )
              resnet_prefix = f"{up_block_prefix}.block.{resnet_idx}"
  
              diffusers_checkpoint.update(
                  vqvae_resnet_to_diffusers_checkpoint(
                      resnet,
                      checkpoint,
                      diffusers_resnet_prefix=diffusers_resnet_prefix,
                      resnet_prefix=resnet_prefix,
                  )
              )
  
          # upsample
  
          # there is no up sample on the last up block
          if diffusers_up_block_idx != len(model.decoder.up_blocks) - 1:
              # There's a single upsample in the VQ-diffusion checkpoint but a list of downsamples
              # in the diffusers model.
              diffusers_downsample_prefix = (
                  f"{diffusers_up_block_prefix}.upsamplers.0.conv"
              )
              downsample_prefix = f"{up_block_prefix}.upsample.conv"
              diffusers_checkpoint.update(
                  {
                      f"{diffusers_downsample_prefix}.weight": checkpoint[
                          f"{downsample_prefix}.weight"
                      ],
                      f"{diffusers_downsample_prefix}.bias": checkpoint[
                          f"{downsample_prefix}.bias"
                      ],
                  }
              )
  
          # attentions
  
          if hasattr(up_block, "attentions"):
              for attention_idx, _ in enumerate(up_block.attentions):
                  diffusers_attention_prefix = f"{diffusers_up_block_prefix}.attentions.{attention_idx}"
                  attention_prefix = (
                      f"{up_block_prefix}.attn.{attention_idx}"
                  )
                  diffusers_checkpoint.update(
                      vqvae_attention_to_diffusers_checkpoint(
                          checkpoint,
                          diffusers_attention_prefix=diffusers_attention_prefix,
                          attention_prefix=attention_prefix,
                      )
                  )
  
      # mid block
  
      # mid block attentions
  
      # There is a single hardcoded attention block in the middle of the VQ-diffusion decoder
      diffusers_attention_prefix = "decoder.mid_block.attentions.0"
      attention_prefix = "decoder.mid.attn_1"
      diffusers_checkpoint.update(
          vqvae_attention_to_diffusers_checkpoint(
              checkpoint,
              diffusers_attention_prefix=diffusers_attention_prefix,
              attention_prefix=attention_prefix,
          )
      )
  
      # mid block resnets
  
      for diffusers_resnet_idx, resnet in enumerate(
          model.encoder.mid_block.resnets
      ):
          diffusers_resnet_prefix = (
              f"decoder.mid_block.resnets.{diffusers_resnet_idx}"
          )
  
          # the hardcoded prefixes to `block_` are 1 and 2
          orig_resnet_idx = diffusers_resnet_idx + 1
          # There are two hardcoded resnets in the middle of the VQ-diffusion decoder
          resnet_prefix = f"decoder.mid.block_{orig_resnet_idx}"
  
          diffusers_checkpoint.update(
              vqvae_resnet_to_diffusers_checkpoint(
                  resnet,
                  checkpoint,
                  diffusers_resnet_prefix=diffusers_resnet_prefix,
                  resnet_prefix=resnet_prefix,
              )
          )
  
      diffusers_checkpoint.update(
          {
              # conv_norm_out
              "decoder.conv_norm_out.weight": checkpoint[
                  "decoder.norm_out.weight"
              ],
              "decoder.conv_norm_out.bias": checkpoint[
                  "decoder.norm_out.bias"
              ],
              # conv_out
              "decoder.conv_out.weight": checkpoint[
                  "decoder.conv_out.weight"
              ],
              "decoder.conv_out.bias": checkpoint[
                  "decoder.conv_out.bias"
              ],
          }
      )
  
      return diffusers_checkpoint
  
imports: import argparse
  import tempfile
  import torch
  import yaml
  from accelerate import init_empty_weights, load_checkpoint_and_dispatch
  from transformers import CLIPTextModel, CLIPTokenizer
  from yaml.loader import FullLoader
  from diffusers import Transformer2DModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
  from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
label_desc: Use image node_id 8 for calling a global function with example usage: vqvae_decoder_to_diffusers_checkpoint(model, checkpoint) and returns: diffusers_checkpoint