diff --git a/sharp_node.py b/sharp_node.py
index a50cbb1..8eba4bb 100644
--- a/sharp_node.py
+++ b/sharp_node.py
@@ -28,10 +28,9 @@ class SharpnessAnalyzer:
             score = cv2.Laplacian(gray, cv2.CV_64F).var()
             scores.append(score)
             
-        # We pass the list of scores to the next node
         return (scores,)
 
-# --- NODE 2: SELECTOR (Uses scores to filter high-res images) ---
+# --- NODE 2: SELECTOR (Filters High-Res images) ---
 class SharpFrameSelector:
     @classmethod
     def INPUT_TYPES(s):
@@ -42,6 +41,8 @@ class SharpFrameSelector:
                 "selection_method": (["batched", "best_n"],),
                 "batch_size": ("INT", {"default": 24, "min": 1, "max": 10000, "step": 1}),
                 "num_frames": ("INT", {"default": 10, "min": 1, "max": 10000, "step": 1}),
+                # NEW SETTING
+                "min_sharpness": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 10000.0, "step": 0.1}),
             }
         }
 
@@ -50,18 +51,17 @@ class SharpFrameSelector:
     FUNCTION = "select_frames"
     CATEGORY = "SharpFrames"
     
-    def select_frames(self, images, scores, selection_method, batch_size, num_frames):
+    def select_frames(self, images, scores, selection_method, batch_size, num_frames, min_sharpness):
         # Validation
         if len(images) != len(scores):
             print(f"[SharpSelector] WARNING: Frame count mismatch! Images: {len(images)}, Scores: {len(scores)}")
-            # If mismatch (e.g. latent optimization), we truncate to the shorter length
             min_len = min(len(images), len(scores))
             images = images[:min_len]
             scores = scores[:min_len]
 
         selected_indices = []
 
-        # --- SELECTION LOGIC (Same as before, but using pre-calculated scores) ---
+        # --- SELECTION LOGIC ---
         if selection_method == "batched":
             total_frames = len(scores)
             for i in range(0, total_frames, batch_size):
@@ -70,14 +70,46 @@ class SharpFrameSelector:
                 
                 # Find best in batch
                 best_in_chunk_idx = np.argmax(chunk_scores)
-                selected_indices.append(i + best_in_chunk_idx)
+                best_score = chunk_scores[best_in_chunk_idx]
+                
+                # Only keep if it passes the threshold
+                if best_score >= min_sharpness:
+                    selected_indices.append(i + best_in_chunk_idx)
                 
         elif selection_method == "best_n":
-            target_count = min(num_frames, len(scores))
-            top_indices = np.argsort(scores)[-target_count:]
-            selected_indices = sorted(top_indices)
+            # 1. Filter out everything below threshold
+            valid_indices = [i for i, s in enumerate(scores) if s >= min_sharpness]
+            
+            # 2. Sort valid candidates by score (Low -> High)
+            # We use numpy array for easy indexing
+            valid_scores = np.array([scores[i] for i in valid_indices])
+            
+            if len(valid_scores) > 0:
+                # How many can we take?
+                target_count = min(num_frames, len(valid_scores))
+                
+                # Get indices of top N scores within the VALID list
+                top_local_indices = np.argsort(valid_scores)[-target_count:]
+                
+                # Map back to global indices
+                top_global_indices = [valid_indices[i] for i in top_local_indices]
+                
+                # Sort by time
+                selected_indices = sorted(top_global_indices)
+            else:
+                selected_indices = []
 
         print(f"[SharpSelector] Selected {len(selected_indices)} frames.")
+        
+        # --- EMPTY RESULT SAFETY NET ---
+        if len(selected_indices) == 0:
+            print("[SharpSelector] Warning: No frames met criteria. Returning 1 black frame to prevent crash.")
+            # Create 1 black pixel frame with same dimensions as input
+            # This keeps the workflow alive
+            h, w = images[0].shape[0], images[0].shape[1]
+            empty_frame = torch.zeros((1, h, w, 3), dtype=images.dtype, device=images.device)
+            return (empty_frame, 0)
+
         result_images = images[selected_indices]
         
         return (result_images, len(selected_indices))
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