add VideoFramePreprocessor

cropper.py

@@ -10,13 +10,13 @@ class VideoFramePreprocessor:
             }
         }
 
-    RETURN_TYPES = ("IMAGE",)
-    RETURN_NAMES = ("processed_images",)
+    # CHANGED: Added three INT outputs for width, height, and frame_count
+    RETURN_TYPES = ("IMAGE", "INT", "INT", "INT")
+    RETURN_NAMES = ("processed_images", "width", "height", "frame_count")
     FUNCTION = "process_frames"
-    CATEGORY = "Stand-In" # Placed in a sub-category for organization
+    CATEGORY = "Stand-In"
 
     def process_frames(self, images: torch.Tensor):
-        # Input tensor shape: (batch_size/frames, height, width, channels)
         if images.dim() != 4:
             raise ValueError("Input must be a batch of images (video frames).")
 
@@ -24,7 +24,6 @@ class VideoFramePreprocessor:
         print(f"Original video specs: {total_frames} frames, {original_w}x{original_h}")
 
         # 1. Trim frame count to be 4n+1
-        # We find the largest number <= total_frames that satisfies the condition.
         new_total_frames = total_frames - ((total_frames - 1) % 4)
         
         if new_total_frames != total_frames:
@@ -33,29 +32,31 @@ class VideoFramePreprocessor:
         else:
             print("Frame count already meets 4n+1 requirement. No trimming needed.")
 
-        # 2. Crop dimensions to the nearest multiple of 8 (rounding down)
-        new_h = (original_h // 8) * 8
-        new_w = (original_w // 8) * 8
+        # 2. Crop dimensions to the nearest multiple of 16 (rounding down)
+        new_h = (original_h // 16) * 16
+        new_w = (original_w // 16) * 16
 
         if new_h != original_h or new_w != original_w:
-            print(f"Cropping dimensions to a multiple of 8: {original_w}x{original_h} -> {new_w}x{new_h}")
+            print(f"Cropping dimensions to a multiple of 16: {original_w}x{original_h} -> {new_w}x{new_h}")
             
-            # Calculate the amount to remove from each side for a center crop
             h_to_remove = original_h - new_h
             w_to_remove = original_w - new_w
             
             h_start = h_to_remove // 2
             w_start = w_to_remove // 2
             
-            # Perform the centered crop using tensor slicing
             processed_images = images[:, h_start : h_start + new_h, w_start : w_start + new_w, :]
         else:
-            print("Dimensions are already multiples of 8. No cropping needed.")
+            print("Dimensions are already multiples of 16. No cropping needed.")
             processed_images = images
             
-        print(f"Final video specs: {processed_images.shape[0]} frames, {processed_images.shape[2]}x{processed_images.shape[1]}")
+        # Get final dimensions from the processed tensor
+        final_frames, final_h, final_w, _ = processed_images.shape
+        
+        print(f"Final video specs: {final_frames} frames, {final_w}x{final_h}")
 
-        return (processed_images,)
+        # CHANGED: Return the processed images along with the final dimensions
+        return (processed_images, final_w, final_h, final_frames)
 
 
 NODE_CLASS_MAPPINGS = {
@@ -64,4 +65,4 @@ NODE_CLASS_MAPPINGS = {
 
 NODE_DISPLAY_NAME_MAPPINGS = {
     "VideoFramePreprocessor": "Stand-In Trimmer & Cropper",
-}
+}