Do not use torch.float64 on the mps device

src/diffusers/schedulers/scheduling_lms_discrete.py

@@ -252,7 +252,8 @@ def add_noise(
     ) -> torch.FloatTensor:
         # Make sure sigmas and timesteps have the same device and dtype as original_samples
         self.sigmas = self.sigmas.to(device=original_samples.device, dtype=original_samples.dtype)
-        self.timesteps = self.timesteps.to(original_samples.device)
+        dtype = torch.float32 if original_samples.device.type == "mps" else timesteps.dtype
+        self.timesteps = self.timesteps.to(original_samples.device, dtype=dtype)
         timesteps = timesteps.to(original_samples.device)
 
         schedule_timesteps = self.timesteps

tests/test_scheduler.py

@@ -27,6 +27,7 @@
     PNDMScheduler,
     ScoreSdeVeScheduler,
 )
+from diffusers.utils import torch_device
 
 
 torch.backends.cuda.matmul.allow_tf32 = False
@@ -258,6 +259,23 @@ def test_scheduler_public_api(self):
             scaled_sample = scheduler.scale_model_input(sample, 0.0)
             self.assertEqual(sample.shape, scaled_sample.shape)
 
+    def test_add_noise_device(self):
+        for scheduler_class in self.scheduler_classes:
+            if scheduler_class == IPNDMScheduler:
+                # Skip until #990 is addressed
+                continue
+            scheduler_config = self.get_scheduler_config()
+            scheduler = scheduler_class(**scheduler_config)
+
+            sample = self.dummy_sample.to(torch_device)
+            scaled_sample = scheduler.scale_model_input(sample, 0.0)
+            self.assertEqual(sample.shape, scaled_sample.shape)
+
+            noise = torch.randn_like(scaled_sample).to(torch_device)
+            t = torch.tensor([10]).to(torch_device)
+            noised = scheduler.add_noise(scaled_sample, noise, t)
+            self.assertEqual(noised.shape, scaled_sample.shape)
+
 
 class DDPMSchedulerTest(SchedulerCommonTest):
     scheduler_classes = (DDPMScheduler,)