diff --git a/comfy/sample.py b/comfy/sample.py
index d6848f9..5e4d261 100644
--- a/comfy/sample.py
+++ b/comfy/sample.py
@@ -1,7 +1,7 @@
 import torch
 import comfy.model_management
 import comfy.samplers
-
+import math
 
 def prepare_noise(latent_image, seed, skip=0):
     """
@@ -16,10 +16,11 @@ def prepare_noise(latent_image, seed, skip=0):
 
 def prepare_mask(noise_mask, shape, device):
     """ensures noise mask is of proper dimensions"""
-    noise_mask = torch.nn.functional.interpolate(noise_mask[None,None,], size=(shape[2], shape[3]), mode="bilinear")
+    noise_mask = torch.nn.functional.interpolate(noise_mask.reshape((-1, 1, noise_mask.shape[-2], noise_mask.shape[-1])), size=(shape[2], shape[3]), mode="bilinear")
     noise_mask = noise_mask.round()
     noise_mask = torch.cat([noise_mask] * shape[1], dim=1)
-    noise_mask = torch.cat([noise_mask] * shape[0])
+    if noise_mask.shape[0] < shape[0]:
+        noise_mask = noise_mask.repeat(math.ceil(shape[0] / noise_mask.shape[0]), 1, 1, 1)[:shape[0]]
     noise_mask = noise_mask.to(device)
     return noise_mask
 
diff --git a/nodes.py b/nodes.py
index b0b61d6..0a9513b 100644
--- a/nodes.py
+++ b/nodes.py
@@ -172,16 +172,12 @@ class VAEEncodeForInpaint:
     def encode(self, vae, pixels, mask):
         x = (pixels.shape[1] // 64) * 64
         y = (pixels.shape[2] // 64) * 64
-        if len(mask.shape) < 3:
-            mask = mask.unsqueeze(0).unsqueeze(0)
-        elif len(mask.shape) < 4:
-            mask = mask.unsqueeze(1)
-        mask = torch.nn.functional.interpolate(mask, size=(pixels.shape[1], pixels.shape[2]), mode="bilinear")
+        mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(pixels.shape[1], pixels.shape[2]), mode="bilinear")
 
         pixels = pixels.clone()
         if pixels.shape[1] != x or pixels.shape[2] != y:
             pixels = pixels[:,:x,:y,:]
-            mask = mask[:,:x,:y,:]
+            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))
@@ -193,7 +189,7 @@ class VAEEncodeForInpaint:
             pixels[:,:,:,i] += 0.5
         t = vae.encode(pixels)
 
-        return ({"samples":t, "noise_mask": (mask_erosion[:,:x,:y,:].round())}, )
+        return ({"samples":t, "noise_mask": (mask_erosion[:,:,:x,:y].round())}, )
 
 class CheckpointLoader:
     @classmethod