Update app.py

app.py

@@ -27,15 +27,15 @@ device = "cuda:0"
 generator = torch.Generator(device=device)
 
 
-models_path = snapshot_download(repo_id="Snapchat/w2w/files")
-
-mean = torch.load(f"{models_path}/mean.pt").bfloat16().to(device)
-std = torch.load(f"{models_path}/std.pt").bfloat16().to(device)
-v = torch.load(f"{models_path}/V.pt").bfloat16().to(device)
-proj = torch.load(f"{models_path}/proj_1000pc.pt").bfloat16().to(device)
-df = torch.load(f"{models_path}/identity_df.pt")
-weight_dimensions = torch.load(f"{models_path}/weight_dimensions.pt")
-pinverse = torch.load(f"{models_path}/pinverse_1000pc.pt").bfloat16().to(device)
+models_path = snapshot_download(repo_id="Snapchat/w2w")
+
+mean = torch.load(f"{models_path}/files/mean.pt").bfloat16().to(device)
+std = torch.load(f"{models_path}/files/std.pt").bfloat16().to(device)
+v = torch.load(f"{models_path}/files/V.pt").bfloat16().to(device)
+proj = torch.load(f"{models_path}/files/proj_1000pc.pt").bfloat16().to(device)
+df = torch.load(f"{models_path}/files/identity_df.pt")
+weight_dimensions = torch.load(f"{models_path}/files/weight_dimensions.pt")
+pinverse = torch.load(f"{models_path}/files/pinverse_1000pc.pt").bfloat16().to(device)
 
 unet, vae, text_encoder, tokenizer, noise_scheduler = load_models(device)
 global network