v3

requirements.txt

@@ -2,4 +2,5 @@ git+https://github.com/huggingface/community-events.git@3fea10c5d5a50c69f509e34c
 transformers
 faiss-cpu
 paddlehub
-paddlepaddle
+paddlepaddle
+torch

utils.py

@@ -1,14 +1,61 @@
 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
 
 ## 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, use_auth_token=None)
+def carga_modelo(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
+    )
+
     gan.eval()
     return gan
 
-
 ## Usamos el modelo GAN para generar imágenes
 def genera(gan, batch_size=1):
     with torch.no_grad():