code: def convert_ldm_original(checkpoint_path, config_path, output_path):
      config = OmegaConf.load(config_path)
      state_dict = torch.load(checkpoint_path, map_location="cpu")[
          "model"
      ]
      keys = list(state_dict.keys())
  
      # extract state_dict for VQVAE
      first_stage_dict = {}
      first_stage_key = "first_stage_model."
      for key in keys:
          if key.startswith(first_stage_key):
              first_stage_dict[
                  key.replace(first_stage_key, "")
              ] = state_dict[key]
  
      # extract state_dict for UNetLDM
      unet_state_dict = {}
      unet_key = "model.diffusion_model."
      for key in keys:
          if key.startswith(unet_key):
              unet_state_dict[key.replace(unet_key, "")] = state_dict[
                  key
              ]
  
      vqvae_init_args = config.model.params.first_stage_config.params
      unet_init_args = config.model.params.unet_config.params
  
      vqvae = VQModel(**vqvae_init_args).eval()
      vqvae.load_state_dict(first_stage_dict)
  
      unet = UNetLDMModel(**unet_init_args).eval()
      unet.load_state_dict(unet_state_dict)
  
      noise_scheduler = DDIMScheduler(
          timesteps=config.model.params.timesteps,
          beta_schedule="scaled_linear",
          beta_start=config.model.params.linear_start,
          beta_end=config.model.params.linear_end,
          clip_sample=False,
      )
  
      pipeline = LDMPipeline(vqvae, unet, noise_scheduler)
      pipeline.save_pretrained(output_path)
  
imports: import argparse
  import OmegaConf
  import torch
  from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
label_desc: Use image node_id 1 for calling a global function with example usage: convert_ldm_original(checkpoint_path, config_path, output_path) without return types