diff --git a/nodes.py b/nodes.py
index a7f9a21..599b306 100644
--- a/nodes.py
+++ b/nodes.py
@@ -1281,7 +1281,7 @@ class BatchCropFromMask:
             "required": {
                 "original_images": ("IMAGE",),
                 "masks": ("MASK",),
-                "bbox_size": ("INT", {"default": 256, "min": 64, "max": 1024, "step": 8}),
+                "crop_size_mult": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
             },
         }
 
@@ -1302,14 +1302,31 @@ class BatchCropFromMask:
     FUNCTION = "crop"
     CATEGORY = "KJNodes/masking"
 
-    def crop(self, masks, original_images, bbox_size):
+    def crop(self, masks, original_images, crop_size_mult):
         bounding_boxes = []
         cropped_images = []
 
+        # First, calculate the maximum bounding box size across all masks
+        max_bbox_size = 0
+        for mask in masks:
+            _mask = tensor2pil(mask)[0]
+            non_zero_indices = np.nonzero(np.array(_mask))
+            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])
+            width = max_x - min_x
+            height = max_y - min_y
+            bbox_size = max(width, height)
+            max_bbox_size = max(max_bbox_size, bbox_size)
+
+        # Make sure max_bbox_size is divisible by 32, if not, round it upwards so it is
+        max_bbox_size = math.ceil(max_bbox_size / 32) * 32
+
+        # Apply the crop size multiplier
+        max_bbox_size = int(max_bbox_size * crop_size_mult)
+
+        # Then, for each mask and corresponding image...
         for mask, img in zip(masks, original_images):
             _mask = tensor2pil(mask)[0]
-
-            # Calculate bounding box coordinates
             non_zero_indices = np.nonzero(np.array(_mask))
             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])
@@ -1318,36 +1335,23 @@ class BatchCropFromMask:
             center_x = (max_x + min_x) // 2
             center_y = (max_y + min_y) // 2
 
-            # Create fixed-size bounding box around center
-            half_box_size = bbox_size // 2
-            min_x = center_x - half_box_size
-            max_x = center_x + half_box_size
-            min_y = center_y - half_box_size
-            max_y = center_y + half_box_size
-
-            # Check if the bounding box dimensions go outside the image dimensions
-            if min_x < 0:
-                max_x -= min_x
-                min_x = 0
-            if max_x > img.shape[1]:
-                min_x -= max_x - img.shape[1]
-                max_x = img.shape[1]
-            if min_y < 0:
-                max_y -= min_y
-                min_y = 0
-            if max_y > img.shape[0]:
-                min_y -= max_y - img.shape[0]
-                max_y = img.shape[0]
+            # Create bounding box using max_bbox_size
+            half_box_size = max_bbox_size // 2
+            min_x = max(0, center_x - half_box_size)
+            max_x = min(img.shape[1], center_x + half_box_size)
+            min_y = max(0, center_y - half_box_size)
+            max_y = min(img.shape[0], center_y + half_box_size)
 
             # Append bounding box coordinates
             bounding_boxes.append((min_x, min_y, max_x - min_x, max_y - min_y))
-            
+
             # Crop the image from the bounding box
             cropped_img = img[min_y:max_y, min_x:max_x, :]
             cropped_images.append(cropped_img)
+
         cropped_out = torch.stack(cropped_images, dim=0)
-        
-        return (original_images, cropped_out, bounding_boxes, bbox_size, bbox_size,)
+
+        return (original_images, cropped_out, bounding_boxes, max_bbox_size, max_bbox_size, )
 
 
 def bbox_to_region(bbox, target_size=None):