diff --git a/comfy_extras/silver_custom.py b/comfy_extras/silver_custom.py
index 03a073c07..c72e99923 100644
--- a/comfy_extras/silver_custom.py
+++ b/comfy_extras/silver_custom.py
@@ -1,5 +1,8 @@
+import PIL
+import numpy as np
 import cv2
 import torch
+from PIL.Image import Image
 
 class ExpandImageMask:
     @classmethod
@@ -12,7 +15,7 @@ class ExpandImageMask:
 
     CATEGORY = "mask"
 
-    RETURN_TYPES = ("IMAGE", "MASK", )
+    RETURN_TYPES = ("MASK", )
     FUNCTION = "image_to_mask_image"
 
     def image_to_mask_image(self, images):
@@ -40,23 +43,34 @@ class ExpandImageMask:
             # 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 3-channel RGB image
+            mask_image_rgb = np.zeros_like(i)
+            mask_image_rgb[:, :, 0] = im_bw
+            mask_image_rgb[:, :, 1] = im_bw
+            mask_image_rgb[:, :, 2] = im_bw
+            pil_image = PIL.Image.fromarray(np.uint8(mask_image_rgb))
 
-            # Convert binary mask image to PyTorch tensor
-            img = torch.from_numpy(im_bw).unsqueeze(0).float()
+            # create a new alpha channel with all pixels set to 255 (fully opaque)
+            alpha = PIL.Image.new('L', pil_image.size, 255)
+
+            # iterate over each pixel and set the alpha channel to 0 if the RGB values are white
+            for x in range(pil_image.width):
+                for y in range(pil_image.height):
+                    if pil_image.getpixel((x, y)) == (255, 255, 255):
+                        alpha.putpixel((x, y), 0)
+
+            # merge the alpha channel with the original image
+            pil_image.putalpha(alpha)
 
             # Append mask image tensor to list
-            mask_images.append(img)
+            mask_images.append(1. - torch.from_numpy(np.array(pil_image.getchannel('A')).astype(np.float32) / 255.0))
 
-        # 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
+        return mask_images
 
 
 NODE_CLASS_MAPPINGS = {
     "ExpandImageMask": ExpandImageMask
 }
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "ExpandImageMask": "Expand Image Mask"
+}