diff --git a/tests/test_modeling_utils.py b/tests/test_modeling_utils.py
index eb9debd18b..92612f9831 100755
--- a/tests/test_modeling_utils.py
+++ b/tests/test_modeling_utils.py
@@ -42,6 +42,18 @@ def floats_tensor(shape, scale=1.0, rng=None, name=None):
 
 
 class ModelTesterMixin(unittest.TestCase):
+
+    @property
+    def dummy_input(self):
+        batch_size = 1
+        num_channels = 3
+        sizes = (32, 32)
+
+        noise = floats_tensor((batch_size, num_channels) + sizes)
+        time_step = torch.tensor([10])
+
+        return (noise, time_step)
+
     def test_from_pretrained_save_pretrained(self):
         config = UNetConfig(dim=8, dim_mults=(1, 2), resnet_block_groups=2)
         model = UNetModel(config)
@@ -50,13 +62,16 @@ class ModelTesterMixin(unittest.TestCase):
             model.save_pretrained(tmpdirname)
             new_model = UNetModel.from_pretrained(tmpdirname)
 
-        batch_size = 1
-        num_channels = 3
-        sizes = (32, 32)
-        noise = floats_tensor((batch_size, num_channels) + sizes)
-        time_step = torch.tensor([10])
+        dummy_input = self.dummy_input
 
-        image = model(noise, time_step)
-        new_image = new_model(noise, time_step)
+        image = model(*dummy_input)
+        new_image = new_model(*dummy_input)
 
         assert (image - new_image).abs().sum() < 1e-5, "Models don't give the same forward pass"
+
+    def test_from_pretrained_hub(self):
+        model = UNetModel.from_pretrained("fusing/ddpm_dummy")
+
+        image = model(*self.dummy_input)
+
+        assert image is not None, "Make sure output is not None"