diff --git a/nodes.py b/nodes.py
index 53e0f74..4f0b7bf 100644
--- a/nodes.py
+++ b/nodes.py
@@ -5,6 +5,7 @@ import sys
 import json
 import hashlib
 import traceback
+import math
 
 from PIL import Image
 from PIL.PngImagePlugin import PngInfo
@@ -223,13 +224,13 @@ class VAEEncodeForInpaint:
 
     @classmethod
     def INPUT_TYPES(s):
-        return {"required": { "pixels": ("IMAGE", ), "vae": ("VAE", ), "mask": ("MASK", )}}
+        return {"required": { "pixels": ("IMAGE", ), "vae": ("VAE", ), "mask": ("MASK", ), "grow_mask_by": ("INT", {"default": 6, "min": 0, "max": 64, "step": 1}),}}
     RETURN_TYPES = ("LATENT",)
     FUNCTION = "encode"
 
     CATEGORY = "latent/inpaint"
 
-    def encode(self, vae, pixels, mask):
+    def encode(self, vae, pixels, mask, grow_mask_by=6):
         x = (pixels.shape[1] // 64) * 64
         y = (pixels.shape[2] // 64) * 64
         mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(pixels.shape[1], pixels.shape[2]), mode="bilinear")
@@ -240,8 +241,14 @@ class VAEEncodeForInpaint:
             mask = mask[:,:,:x,:y]
 
         #grow mask by a few pixels to keep things seamless in latent space
-        kernel_tensor = torch.ones((1, 1, 6, 6))
-        mask_erosion = torch.clamp(torch.nn.functional.conv2d(mask.round(), kernel_tensor, padding=3), 0, 1)
+        if grow_mask_by == 0:
+            mask_erosion = mask
+        else:
+            kernel_tensor = torch.ones((1, 1, grow_mask_by, grow_mask_by))
+            padding = math.ceil((grow_mask_by - 1) / 2)
+
+            mask_erosion = torch.clamp(torch.nn.functional.conv2d(mask.round(), kernel_tensor, padding=padding), 0, 1)
+
         m = (1.0 - mask.round()).squeeze(1)
         for i in range(3):
             pixels[:,:,:,i] -= 0.5