code: def rename_state_dict_key(k):
      k = k.replace("blocks.", "")
      for i in range(5):
          k = k.replace(f"down_{i}_", f"down.{i}.")
          k = k.replace(f"conv_{i}.", f"{i}.")
          k = k.replace(f"up_{i}_", f"up.{i}.")
          k = k.replace(f"mid_{i}", f"mid.{i}")
      k = k.replace("upsamp.", "4.")
      k = k.replace("downsamp.", "3.")
      k = k.replace("f_t.w", "f_t.weight").replace("f_t.b", "f_t.bias")
      k = k.replace("f_1.w", "f_1.weight").replace("f_1.b", "f_1.bias")
      k = k.replace("f_2.w", "f_2.weight").replace("f_2.b", "f_2.bias")
      k = k.replace("f_s.w", "f_s.weight").replace("f_s.b", "f_s.bias")
      k = k.replace("f.w", "f.weight").replace("f.b", "f.bias")
      k = k.replace("gn_1.g", "gn_1.weight").replace(
          "gn_1.b", "gn_1.bias"
      )
      k = k.replace("gn_2.g", "gn_2.weight").replace(
          "gn_2.b", "gn_2.bias"
      )
      k = k.replace("gn.g", "gn.weight").replace("gn.b", "gn.bias")
      return k
  
imports: import math
  import os
  import urllib
  import warnings
  from argparse import ArgumentParser
  import torch
  import torch.nn
  import torch.nn.functional
  from huggingface_hub.utils import insecure_hashlib
  from safetensors.torch import load_file
  from tqdm import tqdm
  from diffusers import AutoencoderKL, ConsistencyDecoderVAE, DiffusionPipeline, StableDiffusionPipeline, UNet2DModel
  from diffusers.models.autoencoders.vae import Encoder
  from diffusers.models.embeddings import TimestepEmbedding
  from diffusers.models.unet_2d_blocks import ResnetDownsampleBlock2D, ResnetUpsampleBlock2D, UNetMidBlock2D
label_desc: Use image node_id 6 for calling a global function with example usage: rename_state_dict_key(k) and returns: k