diff --git a/comfy/model_base.py b/comfy/model_base.py
index 7b2b00a5a..9778392cc 100644
--- a/comfy/model_base.py
+++ b/comfy/model_base.py
@@ -1089,20 +1089,20 @@ class WAN21(BaseModel):
             image = image[:, :(extra_channels - 4)]
 
         mask = kwargs.get("concat_mask", kwargs.get("denoise_mask", None))
-        if mask is None:
-            mask = torch.zeros_like(noise)[:, :4]
-        else:
-            if mask.shape[1] != 4:
-                mask = torch.mean(mask, dim=1, keepdim=True)
-            mask = 1.0 - mask
-            mask = utils.common_upscale(mask.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center")
-            if mask.shape[-3] < noise.shape[-3]:
-                mask = torch.nn.functional.pad(mask, (0, 0, 0, 0, 0, noise.shape[-3] - mask.shape[-3]), mode='constant', value=0)
-            if mask.shape[1] == 1:
-                mask = mask.repeat(1, 4, 1, 1, 1)
+        # if mask is None:
+        #     mask = torch.zeros_like(noise)[:, :4]
+        # else:
+        #     if mask.shape[1] != 4:
+        #         mask = torch.mean(mask, dim=1, keepdim=True)
+        #     mask = 1.0 - mask
+        #     mask = utils.common_upscale(mask.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center")
+        #     if mask.shape[-3] < noise.shape[-3]:
+        #         mask = torch.nn.functional.pad(mask, (0, 0, 0, 0, 0, noise.shape[-3] - mask.shape[-3]), mode='constant', value=0)
+        #     if mask.shape[1] == 1:
+        #         mask = mask.repeat(1, 4, 1, 1, 1)
             
-            print(f"Mask shape: {mask.shape}, noise shape: {noise.shape}")
-            mask = utils.resize_to_batch_size(mask, noise.shape[0])
+        #     print(f"Mask shape: {mask.shape}, noise shape: {noise.shape}")
+        #     mask = utils.resize_to_batch_size(mask, noise.shape[0])
         print(f"image shape: {image.shape}, mask shape: {mask.shape}")
         return torch.cat((mask, image), dim=1)