fork hf model

handler.py

@@ -37,23 +37,22 @@ from huggingface_hub import hf_hub_download
 
 # end_test
 
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+if device.type != 'cuda':
+    raise ValueError("Se requiere ejecutar en GPU")
 
-# hf_hub_download(repo_id="InstantX/InstantID", filename="ControlNetModel/config.json", local_dir="./checkpoints")
-# hf_hub_download(
-#     repo_id="InstantX/InstantID",
-#     filename="ControlNetModel/diffusion_pytorch_model.safetensors",
-#     local_dir="./checkpoints",
-# )
-# hf_hub_download(repo_id="InstantX/InstantID", filename="ip-adapter.bin", local_dir="./checkpoints")
-
-# device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-# if device.type != 'cuda':
-#     raise ValueError("Se requiere ejecutar en GPU")
-
-# dtype = torch.float16 if str(device).__contains__("cuda") else torch.float32
+dtype = torch.float16 if str(device).__contains__("cuda") else torch.float32
 
 class EndpointHandler():
     def __init__(self, model_dir):
+        hf_hub_download(repo_id="InstantX/InstantID", filename="ControlNetModel/config.json", local_dir="./checkpoints")
+        hf_hub_download(
+            repo_id="InstantX/InstantID",
+            filename="ControlNetModel/diffusion_pytorch_model.safetensors",
+            local_dir="./checkpoints",
+        )
+        hf_hub_download(repo_id="InstantX/InstantID", filename="ip-adapter.bin", local_dir="./checkpoints")
+
         print("Model dir: ", model_dir)
         pass
         # face_adapter = f"./checkpoints/ip-adapter.bin"