diff --git a/tests/test_model.py b/tests/test_model.py
index c7b24bee..e5604fce 100644
--- a/tests/test_model.py
+++ b/tests/test_model.py
@@ -358,4 +358,36 @@ def test_gencast_loss():
     preds = torch.rand((batch_size, len(grid_lon), len(grid_lat), features_dim))
     noise_levels = torch.rand((batch_size, 1))
     targets = torch.rand((batch_size, len(grid_lon), len(grid_lat), features_dim))
-    assert loss.forward(preds, targets, noise_levels) is not None
+    assert loss.forward(preds, noise_levels, targets) is not None
+
+
+def test_gencast_denoiser():
+    grid_lat = np.arange(-90, 90, 1)
+    grid_lon = np.arange(0, 360, 1)
+    input_features_dim = 10
+    output_features_dim = 5
+    batch_size = 3
+
+    denoiser = Denoiser(
+        grid_lon=grid_lon,
+        grid_lat=grid_lat,
+        input_features_dim=input_features_dim,
+        output_features_dim=output_features_dim,
+        hidden_dims=[16, 32],
+        num_blocks=3,
+        num_heads=4,
+        splits=0,
+        num_hops=1,
+        device=torch.device("cpu"),
+    ).eval()
+
+    corrupted_targets = torch.randn((batch_size, len(grid_lon), len(grid_lat), output_features_dim))
+    prev_inputs = torch.randn((batch_size, len(grid_lon), len(grid_lat), 2 * input_features_dim))
+    noise_levels = torch.randn((batch_size, 1))
+
+    with torch.no_grad():
+        preds = denoiser(
+            corrupted_targets=corrupted_targets, prev_inputs=prev_inputs, noise_levels=noise_levels
+        )
+
+    assert not torch.isnan(preds).any()
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