test loading model

utils.py

@@ -2,13 +2,64 @@ import streamlit as st
 
 import numpy as np
 import torch
+from huggingface_hub import hf_hub_download
+import json
 
+CONFIG_NAME = "config.json"
+revision = None
+cache_dir = None
+force_download = False
+proxies = None
+resume_download = False
+local_files_only = False
+token = None
 
 
 from huggan.pytorch.lightweight_gan.lightweight_gan import LightweightGAN
 
 def load_model(model_name="ceyda/butterfly_cropped_uniq1K_512"):
-    gan = LightweightGAN.from_pretrained(model_name, use_auth_token=False)
+
+    """
+    Loads a pre-trained LightweightGAN model from Hugging Face Model Hub.
+
+    Args:
+        model_name (str): The name of the pre-trained model to load. Defaults to "ceyda/butterfly_cropped_uniq1K_512".
+        model_version (str): The version of the pre-trained model to load. Defaults to None.
+
+    Returns:
+        LightweightGAN: The loaded pre-trained model.
+    """
+    # Load the config
+    config_file = hf_hub_download(
+        repo_id=str(model_name),
+        filename=CONFIG_NAME,
+        revision=revision,
+        cache_dir=cache_dir,
+        force_download=force_download,
+        proxies=proxies,
+        resume_download=resume_download,
+        token=token,
+        local_files_only=local_files_only,
+    )
+    with open(config_file, "r", encoding="utf-8") as f:
+        config = json.load(f)
+
+    # Call the _from_pretrained with all the needed arguments
+    gan = LightweightGAN(latent_dim=256, image_size=512)
+
+    gan = gan._from_pretrained(
+        model_id=str(model_name),
+        revision=revision,
+        cache_dir=cache_dir,
+        force_download=force_download,
+        proxies=proxies,
+        resume_download=resume_download,
+        local_files_only=local_files_only,
+        token=token,
+        use_auth_token=False,
+        config=config,  # usually in **model_kwargs
+    )
+
     gan.eval()
     return gan