correct diffusion test

src/diffusers/pipelines/pipeline_grad_tts.py

@@ -471,7 +471,7 @@ class GradTTSPipeline(DiffusionPipeline):
         mu_y = mu_y.transpose(1, 2)
 
         # Sample latent representation from terminal distribution N(mu_y, I)
-        z = mu_y + torch.randn(mu_y.shape, device=mu_y.device, generator=generator) / temperature
+        z = mu_y + torch.randn(mu_y.shape, generator=generator).to(mu_y.device)
 
         xt = z * y_mask
         h = 1.0 / num_inference_steps

tests/test_modeling_utils.py

@@ -714,9 +714,9 @@ class PipelineTesterMixin(unittest.TestCase):
 
         assert mel_spec.shape == (1, 80, 143)
         expected_slice = torch.tensor(
-            [-6.6119, -6.5963, -6.2776, -6.7496, -6.7096, -6.5131, -6.4643, -6.4817, -6.7185]
+            [-6.7584, -6.8347, -6.3293, -6.6437, -6.7233, -6.4684, -6.1187, -6.3172, -6.6890]
         )
-        assert (mel_spec[0, :3, :3].flatten() - expected_slice).abs().max() < 1e-2
+        assert (mel_spec[0, :3, :3].cpu().flatten() - expected_slice).abs().max() < 1e-2
 
     def test_module_from_pipeline(self):
         model = DiffWave(num_res_layers=4)