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predict.py

@@ -190,11 +190,11 @@ def predict_next_frame(ens, context_frames: np.ndarray) -> np.ndarray:
                 ens.reset_cache()
             return result
 
-        # Detect scene transitions in context frames
+        # Detect extreme scene transitions (threshold 80 on 0-255 scale)
         scene_transition = False
         for i in range(len(frames) - 1):
             diff = np.abs(frames[i].astype(np.float32) - frames[i + 1].astype(np.float32)).mean()
-            if diff > 30.0 / 255.0:  # frames are normalized to 0-1
+            if diff > 80.0 / 255.0:
                 scene_transition = True
                 break
 
@@ -211,10 +211,10 @@ def predict_next_frame(ens, context_frames: np.ndarray) -> np.ndarray:
             direct_pred = (direct_orig + direct_flipped) / 2.0
 
             if scene_transition:
-                # Scene transition: use direct-only (AR produces garbage after scene changes)
+                # Extreme scene transition: direct-only
                 predicted = direct_pred
             else:
-                # Normal scene: full AR+direct blend with noise diversity
+                # Normal: full AR+direct blend with noise diversity
                 all_ar_runs = []
                 for noise_std in [0.0, 1.0/255.0, 2.0/255.0]:
                     ar_preds_run = []