diff --git a/src/diffusers/models/unet_grad_tts.py b/src/diffusers/models/unet_grad_tts.py
index 84bd64d9d0..a2bdd951e4 100644
--- a/src/diffusers/models/unet_grad_tts.py
+++ b/src/diffusers/models/unet_grad_tts.py
@@ -190,7 +190,7 @@ class UNetGradTTSModel(ModelMixin, ConfigMixin):
         self.final_block = Block(dim, dim)
         self.final_conv = torch.nn.Conv2d(dim, 1, 1)
 
-    def forward(self, x, mask, mu, t, spk=None):
+    def forward(self, x, timesteps, mu, mask, spk=None):
         if self.n_spks > 1:
             # Get speaker embedding
             spk = self.spk_emb(spk)
@@ -198,7 +198,7 @@ class UNetGradTTSModel(ModelMixin, ConfigMixin):
         if not isinstance(spk, type(None)):
             s = self.spk_mlp(spk)
 
-        t = self.time_pos_emb(t, scale=self.pe_scale)
+        t = self.time_pos_emb(timesteps, scale=self.pe_scale)
         t = self.mlp(t)
 
         if self.n_spks < 2:
diff --git a/src/diffusers/pipelines/pipeline_grad_tts.py b/src/diffusers/pipelines/pipeline_grad_tts.py
index c9fad2192c..4201124923 100644
--- a/src/diffusers/pipelines/pipeline_grad_tts.py
+++ b/src/diffusers/pipelines/pipeline_grad_tts.py
@@ -472,7 +472,7 @@ class GradTTS(DiffusionPipeline):
             t = (1.0 - (t + 0.5) * h) * torch.ones(z.shape[0], dtype=z.dtype, device=z.device)
             time = t.unsqueeze(-1).unsqueeze(-1)
 
-            residual = self.unet(xt, y_mask, mu_y, t, speaker_id)
+            residual = self.unet(xt, t, mu_y, y_mask, speaker_id)
 
             xt = self.noise_scheduler.step(xt, residual, mu_y, h, time)
             xt = xt * y_mask
diff --git a/tests/test_modeling_utils.py b/tests/test_modeling_utils.py
index d6e90ea55a..7b2530a75e 100755
--- a/tests/test_modeling_utils.py
+++ b/tests/test_modeling_utils.py
@@ -35,6 +35,7 @@ from diffusers import (
     PNDMScheduler,
     UNetModel,
     UNetLDMModel,
+    UNetGradTTSModel,
 )
 from diffusers.configuration_utils import ConfigMixin
 from diffusers.pipeline_utils import DiffusionPipeline
@@ -410,6 +411,78 @@ class UNetLDMModelTests(ModelTesterMixin, unittest.TestCase):
         self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3))
 
 
+class UNetGradTTSModelTests(ModelTesterMixin, unittest.TestCase):
+    model_class = UNetGradTTSModel
+
+    @property
+    def dummy_input(self):
+        batch_size = 4
+        num_features = 32
+        seq_len = 16
+
+        noise = floats_tensor((batch_size, num_features, seq_len)).to(torch_device)
+        condition = floats_tensor((batch_size, num_features, seq_len)).to(torch_device)
+        mask = floats_tensor((batch_size, 1, seq_len)).to(torch_device)
+        time_step = torch.tensor([10] * batch_size).to(torch_device)
+
+        return {"x": noise, "timesteps": time_step, "mu": condition, "mask": mask}
+
+    @property
+    def get_input_shape(self):
+        return (4, 32, 16)
+
+    @property
+    def get_output_shape(self):
+        return (4, 32, 16)
+
+    def prepare_init_args_and_inputs_for_common(self):
+        init_dict = {
+            "dim": 64,
+            "groups": 4,
+            "dim_mults": (1, 2),
+            "n_feats": 32,
+            "pe_scale": 1000,
+            "n_spks": 1,
+        }
+        inputs_dict = self.dummy_input
+        return init_dict, inputs_dict
+    
+    def test_from_pretrained_hub(self):
+        model, loading_info = UNetGradTTSModel.from_pretrained("fusing/unet-grad-tts-dummy", 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 = UNetGradTTSModel.from_pretrained("fusing/unet-grad-tts-dummy")
+        model.eval()
+
+        torch.manual_seed(0)
+        if torch.cuda.is_available():
+            torch.cuda.manual_seed_all(0)
+        
+        num_features = model.config.n_feats
+        seq_len = 16
+        noise = torch.randn((1, num_features, seq_len))
+        condition = torch.randn((1, num_features, seq_len))
+        mask = torch.randn((1, 1, seq_len))
+        time_step = torch.tensor([10])
+
+        with torch.no_grad():
+            output = model(noise, time_step, condition, mask)
+
+        output_slice = output[0, -3:, -3:].flatten()
+        # fmt: off
+        expected_output_slice = torch.tensor([-0.0690, -0.0531,  0.0633, -0.0660, -0.0541,  0.0650, -0.0656, -0.0555, 0.0617])
+        # fmt: on
+
+        self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3))
+
+
 class PipelineTesterMixin(unittest.TestCase):
     def test_from_pretrained_save_pretrained(self):
         # 1. Load models