first commit

model.py

@@ -37,8 +37,6 @@ sys.path.insert(0, mapper_dir.as_posix())
 from mapper.datasets.latents_dataset_inference import LatentsDatasetInference
 from mapper.hairclip_mapper import HairCLIPMapper
 
-HF_TOKEN = os.environ['HF_TOKEN']
-
 
 class Model:
     def __init__(self, device: Union[torch.device, str]):
@@ -51,15 +49,15 @@ class Model:
     @staticmethod
     def _create_dlib_landmark_model():
         path = huggingface_hub.hf_hub_download(
-            'hysts/dlib_face_landmark_model',
-            'shape_predictor_68_face_landmarks.dat',
-            use_auth_token=HF_TOKEN)
+            'aijack/jojogan',
+            'face_landmarks.dat'
+           )
         return dlib.shape_predictor(path)
 
     def _load_e4e(self) -> nn.Module:
-        ckpt_path = huggingface_hub.hf_hub_download('hysts/e4e',
-                                                    'e4e_ffhq_encode.pt',
-                                                    use_auth_token=HF_TOKEN)
+        ckpt_path = huggingface_hub.hf_hub_download('aijack/e4e',
+                                                    'e4e.pt'
+                                                    )
         ckpt = torch.load(ckpt_path, map_location='cpu')
         opts = ckpt['opts']
         opts['device'] = self.device.type
@@ -71,9 +69,9 @@ class Model:
         return model
 
     def _load_hairclip(self) -> nn.Module:
-        ckpt_path = huggingface_hub.hf_hub_download('hysts/HairCLIP',
-                                                    'hairclip.pt',
-                                                    use_auth_token=HF_TOKEN)
+        ckpt_path = huggingface_hub.hf_hub_download('aijack/hair',
+                                                    'hairclip.pt'
+                                                    )
         ckpt = torch.load(ckpt_path, map_location='cpu')
         opts = ckpt['opts']
         opts['device'] = self.device.type