diff --git a/src/diffusers/models/unet_rl.py b/src/diffusers/models/unet_rl.py
index 9c0c7713..56264128 100644
--- a/src/diffusers/models/unet_rl.py
+++ b/src/diffusers/models/unet_rl.py
@@ -195,7 +195,7 @@ class TemporalUNet(ModelMixin, ConfigMixin):  # (nn.Module):
             nn.Conv1d(dim, transition_dim, 1),
         )
 
-    def forward(self, x, cond, time):
+    def forward(self, x, time):
         """
         x : [ batch x horizon x transition ]
         """
diff --git a/tests/test_modeling_utils.py b/tests/test_modeling_utils.py
index be7f61da..70e3ec3b 100755
--- a/tests/test_modeling_utils.py
+++ b/tests/test_modeling_utils.py
@@ -40,6 +40,7 @@ from diffusers import (
     ScoreSdeVeScheduler,
     ScoreSdeVpPipeline,
     ScoreSdeVpScheduler,
+    TemporalUNet,
     UNetGradTTSModel,
     UNetLDMModel,
     UNetModel,
@@ -606,6 +607,46 @@ class UNetGradTTSModelTests(ModelTesterMixin, unittest.TestCase):
         self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3))
 
 
+class TemporalUNetModelTests(ModelTesterMixin, unittest.TestCase):
+    model_class = TemporalUNet
+
+    def test_from_pretrained_hub(self):
+        model, loading_info = TemporalUNet.from_pretrained(
+            "fusing/ddpm-unet-rl-hopper-hor128", output_loading_info=True
+        )
+        self.assertIsNotNone(model)
+        self.assertEqual(len(loading_info["missing_keys"]), 0)
+
+        model.to(torch_device)
+        image = model(**self.dummy_input)
+
+        assert image is not None, "Make sure output is not None"
+
+    def test_output_pretrained(self):
+        model = TemporalUNet.from_pretrained("fusing/ddpm-unet-rl-hopper-hor128")
+        model.eval()
+
+        torch.manual_seed(0)
+        if torch.cuda.is_available():
+            torch.cuda.manual_seed_all(0)
+
+        num_features = model.transition_dim
+        seq_len = 16
+        noise = torch.randn((1, seq_len, num_features))
+        time_step = torch.full((num_features,), 0)
+
+        with torch.no_grad():
+            output = model(noise, time_step)
+
+        output_slice = output[0, -3:, -3:].flatten()
+        # fmt: off
+        expected_output_slice = torch.tensor([-0.2714, 0.1042, -0.0794, -0.2820, 0.0803, -0.0811, -0.2345, 0.0580,
+                                              -0.0584])
+        # fmt: on
+
+        self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3))
+
+
 class NCSNppModelTests(ModelTesterMixin, unittest.TestCase):
     model_class = NCSNpp