diff --git a/vr180_streaming/sam2_streaming_simple.py b/vr180_streaming/sam2_streaming_simple.py
index 60f1e30..a8402e4 100644
--- a/vr180_streaming/sam2_streaming_simple.py
+++ b/vr180_streaming/sam2_streaming_simple.py
@@ -28,25 +28,17 @@ class SAM2StreamingProcessor:
         self.device = torch.device(config.get('hardware', {}).get('device', 'cuda'))
         
         # SAM2 model configuration
-        model_cfg_name = config.get('matting', {}).get('sam2_model_cfg', 'sam2.1_hiera_l')
+        model_cfg = config.get('matting', {}).get('sam2_model_cfg', 'sam2.1_hiera_l')
         checkpoint = config.get('matting', {}).get('sam2_checkpoint', 
                                                    'segment-anything-2/checkpoints/sam2.1_hiera_large.pt')
         
-        # Map config name to full path
-        config_mapping = {
-            'sam2.1_hiera_t': 'segment-anything-2/sam2/configs/sam2.1/sam2.1_hiera_t.yaml',
-            'sam2.1_hiera_s': 'segment-anything-2/sam2/configs/sam2.1/sam2.1_hiera_s.yaml',
-            'sam2.1_hiera_b+': 'segment-anything-2/sam2/configs/sam2.1/sam2.1_hiera_b+.yaml',
-            'sam2.1_hiera_l': 'segment-anything-2/sam2/configs/sam2.1/sam2.1_hiera_l.yaml',
-        }
-        
-        model_cfg = config_mapping.get(model_cfg_name, model_cfg_name)
-        
         # Build predictor (simple, clean approach)
+        # Note: SAM2 uses Hydra to find configs automatically in sam2/configs/
         self.predictor = build_sam2_video_predictor(
-            model_cfg,
+            model_cfg,  # Just the config name, Hydra finds it automatically
             checkpoint,
-            device=self.device
+            device=self.device,
+            vos_optimized=True  # Enable VOS optimizations for speed
         )
         
         # Frame buffer for streaming (like det-sam2)