content: """ Utiliy functions to load pre-trained models more easily """
  import os
  import pkg_resources
  from omegaconf import OmegaConf
  
  import torch
  from huggingface_hub import hf_hub_download
  
  from CRM_T2I_V3.imagedream.ldm.util import instantiate_from_config
  
  
  PRETRAINED_MODELS = {
      "sd-v2.1-base-4view-ipmv": {
          "config": "sd_v2_base_ipmv.yaml",
          "repo_id": "Peng-Wang/ImageDream",
          "filename": "sd-v2.1-base-4view-ipmv.pt",
      },
      "sd-v2.1-base-4view-ipmv-local": {
          "config": "sd_v2_base_ipmv_local.yaml",
          "repo_id": "Peng-Wang/ImageDream",
          "filename": "sd-v2.1-base-4view-ipmv-local.pt",
      },
  }
  
  
  def get_config_file(config_path):
      cfg_file = pkg_resources.resource_filename(
          "imagedream", os.path.join("configs", config_path)
      )
      if not os.path.exists(cfg_file):
          raise RuntimeError(f"Config {config_path} not available!")
      return cfg_file
  
  
  def build_model(model_name, config_path=None, ckpt_path=None, cache_dir=None):
      if (config_path is not None) and (ckpt_path is not None):
          config = OmegaConf.load(config_path)
          model = instantiate_from_config(config.model)
          model.load_state_dict(torch.load(ckpt_path, map_location="cpu"), strict=False)
          return model
          
      if not model_name in PRETRAINED_MODELS:
          raise RuntimeError(
              f"Model name {model_name} is not a pre-trained model. Available models are:\n- "
              + "\n- ".join(PRETRAINED_MODELS.keys())
          )
      model_info = PRETRAINED_MODELS[model_name]
  
      # Instiantiate the model
      print(f"Loading model from config: {model_info['config']}")
      config_file = get_config_file(model_info["config"])
      config = OmegaConf.load(config_file)
      model = instantiate_from_config(config.model)
  
      # Load pre-trained checkpoint from huggingface
      if not ckpt_path:
          ckpt_path = hf_hub_download(
              repo_id=model_info["repo_id"],
              filename=model_info["filename"],
              cache_dir=cache_dir,
          )
          print(f"Loading model from cache file: {ckpt_path}")
      model.load_state_dict(torch.load(ckpt_path, map_location="cpu"), strict=False)
      return model
  