privatize weights

app.py

@@ -183,13 +183,14 @@ class HandDiffOpts:
     n_val_samples: int = 4
 
 # load models
+token = os.getenv("HF_TOKEN")
 if NEW_MODEL:
     opts = HandDiffOpts()
     if MODEL_EPOCH == 7:
         model_path = './DINO_EMA_11M_b50_lr1e-5_epoch7_step380k.ckpt'
     elif MODEL_EPOCH == 6:
         # model_path = "./DINO_EMA_11M_b50_lr1e-5_epoch6_step320k.ckpt"
-        model_path = hf_hub_download(repo_id="Chaerin5/FoundHand-weights", filename="DINO_EMA_11M_b50_lr1e-5_epoch6_step320k.ckpt")
+        model_path = hf_hub_download(repo_id="Chaerin5/FoundHand-weights", filename="DINO_EMA_11M_b50_lr1e-5_epoch6_step320k.ckpt", token=token)
     elif MODEL_EPOCH == 4:
         model_path = "./DINO_EMA_11M_b50_lr1e-5_epoch4_step210k.ckpt"
     elif MODEL_EPOCH == 10:
@@ -239,7 +240,7 @@ else:
     missing_keys, extra_keys = autoencoder.load_state_dict(vae_state_dict, strict=False)
     autoencoder.eval()
     assert len(missing_keys) == 0 and len(extra_keys) == 0
-sam_path = hf_hub_download(repo_id="Chaerin5/FoundHand-weights", filename="sam_vit_h_4b8939.pth")
+sam_path = hf_hub_download(repo_id="Chaerin5/FoundHand-weights", filename="sam_vit_h_4b8939.pth", token=token)
 sam_predictor = init_sam(ckpt_path=sam_path, device=device)