diff --git a/comfy_extras/silver_custom.py b/comfy_extras/silver_custom.py
index 370b07138..03a073c07 100644
--- a/comfy_extras/silver_custom.py
+++ b/comfy_extras/silver_custom.py
@@ -6,43 +6,56 @@ class ExpandImageMask:
     def INPUT_TYPES(s):
         return {
                 "required": {
-                    "images": ("IMAGE",)
+                    "images": ("IMAGE", )
                 }
         }
 
     CATEGORY = "mask"
 
-    RETURN_TYPES = ("IMAGE",)
+    RETURN_TYPES = ("IMAGE", "MASK", )
     FUNCTION = "image_to_mask_image"
 
     def image_to_mask_image(self, images):
         mask_images = []
         for image in images:
             i = 255. * image.cpu().numpy()
-            # opencv_image = PIL.Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
-            # cv2.imwrite('opencv_image.png', i)
 
+            # Convert to grayscale
             image_gray = cv2.cvtColor(i, cv2.COLOR_BGR2GRAY)
-            image_gray = cv2.blur(image_gray, (10, 10))
-            # cv2.imwrite('image_gray1.png', image_gray)
-            image_gray = cv2.blur(image_gray, (20, 20))
-            # cv2.imwrite('image_gray2.png', image_gray)
 
-            # Convert the image to the expected data type
+            # Apply blurring to grayscale image
+            image_gray = cv2.blur(image_gray, (10, 10))
+            image_gray = cv2.blur(image_gray, (20, 20))
+
+            # Convert image to the expected data type
             image_gray = cv2.convertScaleAbs(image_gray)
 
-            # Apply the threshold using the modified image
+            # Apply threshold to grayscale image
             (thresh, im_bw) = cv2.threshold(image_gray, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)
+
+            # Apply blurring to binary mask image
             ksize = (50, 50)
             im_bw = cv2.blur(im_bw, ksize)
-            im_bw = cv2.threshold(im_bw, thresh, 255, cv2.THRESH_BINARY)[1]
-            im_bw = cv2.bitwise_not(im_bw)
-            # cv2.imwrite('im_bw.png', im_bw)
 
-            # Convert the binary mask image to a PyTorch tensor
+            # Threshold binary mask image again
+            im_bw = cv2.threshold(im_bw, thresh, 255, cv2.THRESH_BINARY)[1]
+
+            # Invert binary mask image
+            # im_bw = cv2.bitwise_not(im_bw)
+
+            # Convert binary mask image to PyTorch tensor
             img = torch.from_numpy(im_bw).unsqueeze(0).float()
+
+            # Append mask image tensor to list
             mask_images.append(img)
-        return tuple(mask_images)
+
+        # Stack list of mask image tensors into a single tensor
+        mask_images_tensor = torch.cat(mask_images)
+
+        # Return tuple of mask images and single mask image
+        single_mask_image = mask_images_tensor[0, :, :]
+        return mask_images_tensor, single_mask_image
+
 
 NODE_CLASS_MAPPINGS = {
     "ExpandImageMask": ExpandImageMask