diff --git a/nerfstudio/models/splatfacto.py b/nerfstudio/models/splatfacto.py
index e1f64ebcb..27319fb91 100644
--- a/nerfstudio/models/splatfacto.py
+++ b/nerfstudio/models/splatfacto.py
@@ -645,6 +645,17 @@ class SplatfactoModel(Model):
         else:
             return 1
 
+    def _downscale_if_required(self, image):
+        d = self._get_downscale_factor()
+        if d > 1:
+            newsize = [image.shape[0] // d, image.shape[1] // d]
+
+            # torchvision can be slow to import, so we do it lazily.
+            import torchvision.transforms.functional as TF
+
+            return TF.resize(image.permute(2, 0, 1), newsize, antialias=None).permute(1, 2, 0)
+        return image
+
     def get_outputs(self, camera: Cameras) -> Dict[str, Union[torch.Tensor, List]]:
         """Takes in a Ray Bundle and returns a dictionary of outputs.
 
@@ -818,16 +829,7 @@ class SplatfactoModel(Model):
         """
         if image.dtype == torch.uint8:
             image = image.float() / 255.0
-        d = self._get_downscale_factor()
-        if d > 1:
-            newsize = [image.shape[0] // d, image.shape[1] // d]
-
-            # torchvision can be slow to import, so we do it lazily.
-            import torchvision.transforms.functional as TF
-
-            gt_img = TF.resize(image.permute(2, 0, 1), newsize, antialias=None).permute(1, 2, 0)
-        else:
-            gt_img = image
+        gt_img = self._downscale_if_required(image)
         return gt_img.to(self.device)
 
     def composite_with_background(self, image, background) -> torch.Tensor:
@@ -873,8 +875,9 @@ class SplatfactoModel(Model):
         # This is a little bit sketchy for the SSIM loss.
         if "mask" in batch:
             # batch["mask"] : [H, W, 1]
-            assert batch["mask"].shape[:2] == gt_img.shape[:2] == pred_img.shape[:2]
-            mask = batch["mask"].to(self.device)
+            mask = self._downscale_if_required(batch["mask"])
+            mask = mask.to(self.device)
+            assert mask.shape[:2] == gt_img.shape[:2] == pred_img.shape[:2]
             gt_img = gt_img * mask
             pred_img = pred_img * mask