Update app.py

app.py

@@ -28,18 +28,18 @@ device = 'cuda' if torch.cuda.is_available() else 'cpu'
 outdir = "/tmp/rayst3r"
 
 moge_model = MoGeModel.from_pretrained("Ruicheng/moge-vitl").to(device)
-# dino_model = torch.hub.load('facebookresearch/dinov2', "dinov2_vitl14_reg")
-# dino_model.eval()
-# dino_model.to(device)
+dino_model = torch.hub.load('facebookresearch/dinov2', "dinov2_vitl14_reg")
+dino_model.eval()
+dino_model.to(device)
 
-# print("Loading RaySt3R model")
-# rayst3r_checkpoint = hf_hub_download("bartduis/rayst3r", "rayst3r.pth")
-# rayst3r_model = EvalWrapper(rayst3r_checkpoint,device='cpu')
-# rayst3r_model = rayst3r_model.to(device)
-# print("Loaded all models")
+print("Loading RaySt3R model")
+rayst3r_checkpoint = hf_hub_download("bartduis/rayst3r", "rayst3r.pth")
+rayst3r_model = EvalWrapper(rayst3r_checkpoint,device='cpu')
+rayst3r_model = rayst3r_model.to(device)
+print("Loaded all models")
 
-# print("Loading MoGe model")
-# # Load the model from huggingface hub (or load from local).
+print("Loading MoGe model")
+# Load the model from huggingface hub (or load from local).
 
 
 def depth2uint16(depth):
@@ -112,8 +112,6 @@ def rayst3r_to_glb(img,depth_dict,mask,max_total_points=10e6,rotated=False):
     prep_for_rayst3r(img,depth_dict,mask)
     print('Doneneee')
     
-    
-    
     # rayst3r_points = eval_scene(rayst3r_model,os.path.join(outdir, "input"),do_filter_all_masks=True,dino_model=dino_model, device = device).cpu()
 
     # # subsample points