Update models/pipeline_controlvideo.py

models/pipeline_controlvideo.py

@@ -670,43 +670,43 @@ class ControlVideoPipeline(DiffusionPipeline):
         return key_frame_indices, inter_frame_list
     """
     def get_slide_window_indices(self, video_length, window_size):
-    assert window_size >= 3
-
-    # Define the chunk size for processing
-    chunk_size = 4
-
-    # Calculate the number of chunks
-    num_chunks = (video_length - 1) // chunk_size + 1
-
-    # Initialize the lists to store the results
-    key_frame_indices = []
-    inter_frame_list = []
-
-    for chunk_index in range(num_chunks):
-        # Calculate the start and end indices for the current chunk
-        start_index = chunk_index * chunk_size
-        end_index = min((chunk_index + 1) * chunk_size, video_length)
-
-        # Generate key frame indices for the current chunk
-        chunk_key_frame_indices = np.arange(start_index, end_index, window_size - 1).tolist()
-
-        # Append the last index if it's not already included
-        if chunk_key_frame_indices[-1] != (end_index - 1):
-            chunk_key_frame_indices.append(end_index - 1)
-
-        # Append the key frame indices of the current chunk to the overall list
-        key_frame_indices.extend(chunk_key_frame_indices)
-
-        # Generate slices for the current chunk
-        chunk_slices = np.split(np.arange(start_index, end_index), chunk_key_frame_indices)
-
-        # Process each slice in the current chunk
-        for s in chunk_slices:
-            if len(s) < 2:
-                continue
-            inter_frame_list.append(s[1:].tolist())
-
-    return key_frame_indices, inter_frame_list
+        assert window_size >= 3
+    
+        # Define the chunk size for processing
+        chunk_size = 4
+    
+        # Calculate the number of chunks
+        num_chunks = (video_length - 1) // chunk_size + 1
+    
+        # Initialize the lists to store the results
+        key_frame_indices = []
+        inter_frame_list = []
+    
+        for chunk_index in range(num_chunks):
+            # Calculate the start and end indices for the current chunk
+            start_index = chunk_index * chunk_size
+            end_index = min((chunk_index + 1) * chunk_size, video_length)
+    
+            # Generate key frame indices for the current chunk
+            chunk_key_frame_indices = np.arange(start_index, end_index, window_size - 1).tolist()
+    
+            # Append the last index if it's not already included
+            if chunk_key_frame_indices[-1] != (end_index - 1):
+                chunk_key_frame_indices.append(end_index - 1)
+    
+            # Append the key frame indices of the current chunk to the overall list
+            key_frame_indices.extend(chunk_key_frame_indices)
+    
+            # Generate slices for the current chunk
+            chunk_slices = np.split(np.arange(start_index, end_index), chunk_key_frame_indices)
+    
+            # Process each slice in the current chunk
+            for s in chunk_slices:
+                if len(s) < 2:
+                    continue
+                inter_frame_list.append(s[1:].tolist())
+    
+        return key_frame_indices, inter_frame_list
     @torch.no_grad()
     def __call__(
         self,