diff --git a/nodes/image_nodes.py b/nodes/image_nodes.py
index 0a6869d..865d777 100644
--- a/nodes/image_nodes.py
+++ b/nodes/image_nodes.py
@@ -2474,21 +2474,52 @@ class ImageCropByMaskAndResize:
         return (int(x0), int(y0), int(w), int(h))
 
     def crop(self, image, mask, base_resolution, padding=0, min_crop_resolution=128, max_crop_resolution=512):
+        mask = mask.round()
         image_list = []
         mask_list = []
         bbox_list = []
+
+        # First, collect all bounding boxes
+        bbox_params = []
+        aspect_ratios = []
         for i in range(image.shape[0]):
             x0, y0, w, h = self.crop_by_mask(mask[i], padding, min_crop_resolution, max_crop_resolution)
-            cropped_image = image[i][y0:y0+h, x0:x0+w, :]
-            cropped_mask = mask[i][y0:y0+h, x0:x0+w]
-            
-            aspect_ratio = w / h
-            if aspect_ratio > 1:
-                target_width = base_resolution
-                target_height = int(base_resolution / aspect_ratio)
-            else:
-                target_height = base_resolution
-                target_width = int(base_resolution * aspect_ratio)
+            bbox_params.append((x0, y0, w, h))
+            aspect_ratios.append(w / h)
+        #print(bbox_params)
+
+        # Find maximum width and height
+        max_w = max([w for x0, y0, w, h in bbox_params])
+        max_h = max([h for x0, y0, w, h in bbox_params])
+        max_aspect_ratio = max(aspect_ratios)
+
+        # Ensure dimensions are divisible by 8
+        max_w = (max_w + 7) // 8 * 8
+        max_h = (max_h + 7) // 8 * 8
+        # Calculate common target dimensions
+        if max_aspect_ratio > 1:
+            target_width = base_resolution
+            target_height = int(base_resolution / max_aspect_ratio)
+        else:
+            target_height = base_resolution
+            target_width = int(base_resolution * max_aspect_ratio)
+
+        for i in range(image.shape[0]):
+            x0, y0, w, h = bbox_params[i]
+
+            # Adjust cropping to use maximum width and height
+            x_center = x0 + w / 2
+            y_center = y0 + h / 2
+
+            x0_new = int(max(0, x_center - max_w / 2))
+            y0_new = int(max(0, y_center - max_h / 2))
+            x1_new = int(min(x0_new + max_w, image.shape[2]))
+            y1_new = int(min(y0_new + max_h, image.shape[1]))
+            x0_new = x1_new - max_w
+            y0_new = y1_new - max_h
+
+            cropped_image = image[i][y0_new:y1_new, x0_new:x1_new, :]
+            cropped_mask = mask[i][y0_new:y1_new, x0_new:x1_new]
             
             # Ensure dimensions are divisible by 8
             target_width = (target_width + 7) // 8 * 8
@@ -2504,7 +2535,8 @@ class ImageCropByMaskAndResize:
 
             image_list.append(cropped_image)
             mask_list.append(cropped_mask)
-            bbox_list.append((x0, y0, x0 + w, y0 + h))
+            bbox_list.append((x0_new, y0_new, x1_new, y1_new))
+
 
         return (torch.stack(image_list), torch.stack(mask_list), bbox_list)