diff --git a/nodes.py b/nodes.py
index 3d76f12..612d0bd 100644
--- a/nodes.py
+++ b/nodes.py
@@ -1903,14 +1903,8 @@ class BatchCropFromMaskAdvanced:
             _mask = tensor2pil(mask)[0]
             non_zero_indices = np.nonzero(np.array(_mask))
 
-            # handle empty masks
-            if len(non_zero_indices[0]) == 0 or len(non_zero_indices[1]) == 0:
-                bounding_boxes.append((0, 0, img.shape[1], img.shape[0]))
-                cropped_images.append(img)
-                cropped_masks.append(mask)
-                combined_cropped_images.append(img)
-                combined_cropped_masks.append(mask)
-            else:
+            # check for empty masks
+            if len(non_zero_indices[0]) > 0 and len(non_zero_indices[1]) > 0:
                 min_x, max_x = np.min(non_zero_indices[1]), np.max(non_zero_indices[1])
                 min_y, max_y = np.min(non_zero_indices[0]), np.max(non_zero_indices[0])
 
@@ -1966,6 +1960,12 @@ class BatchCropFromMaskAdvanced:
 
                 combined_cropped_mask = masks[i][new_min_y:new_max_y, new_min_x:new_max_x]
                 combined_cropped_masks.append(combined_cropped_mask)
+            else:
+                bounding_boxes.append((0, 0, img.shape[1], img.shape[0]))
+                cropped_images.append(img)
+                cropped_masks.append(mask)
+                combined_cropped_images.append(img)
+                combined_cropped_masks.append(mask)
 
         cropped_out = torch.stack(cropped_images, dim=0)
         combined_crop_out = torch.stack(combined_cropped_images, dim=0)