Update utils.py

utils.py

@@ -1,18 +1,74 @@
+import json
+
 import numpy as np
 import torch
 from huggan.pytorch.lightweight_gan.lightweight_gan import LightweightGAN
+from huggingface_hub import hf_hub_download
+
+CONFIG_NAME = "config.json"
+revision = None
+cache_dir = None
+force_download = False
+proxies = None
+resume_download = False
+local_files_only = False
+token = None
+
+
+def load_model(model_name="ceyda/butterfly_cropped_uniq1K_512"):
+    """
+    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
+    )
 
-## Cargamos el modelo desde el Hub de Hugging Face
-def carga_modelo(model_name="ceyda/butterfly_cropped_uniq1K_512", model_version=None):
-    gan = LightweightGAN.from_pretrained(model_name, version=model_version)
     gan.eval()
     return gan
 
 
-## Usamos el modelo GAN para generar imágenes
-def genera(gan, batch_size=1):
+def generate(gan, batch_size=1):
+    """
+    Generates images using the given GAN model.
+    Args:
+        gan (nn.Module): The GAN model to use for generating images.
+        batch_size (int, optional): The number of images to generate in each batch. Defaults to 1.
+    Returns:
+        numpy.ndarray: A numpy array of generated images.
+    """
     with torch.no_grad():
         ims = gan.G(torch.randn(batch_size, gan.latent_dim)).clamp_(0.0, 1.0) * 255
         ims = ims.permute(0, 2, 3, 1).detach().cpu().numpy().astype(np.uint8)
-    return ims
+    return ims