handle device for randn in euler step (#1124)

* handle device for randn in euler step

* convert device to str

src/diffusers/schedulers/scheduling_euler_ancestral_discrete.py

@@ -217,7 +217,16 @@ class EulerAncestralDiscreteScheduler(SchedulerMixin, ConfigMixin):
         prev_sample = sample + derivative * dt
 
         device = model_output.device if torch.is_tensor(model_output) else "cpu"
-        noise = torch.randn(model_output.shape, dtype=model_output.dtype, generator=generator).to(device)
+        if str(device) == "mps":
+            # randn does not work reproducibly on mps
+            noise = torch.randn(model_output.shape, dtype=model_output.dtype, device="cpu", generator=generator).to(
+                device
+            )
+        else:
+            noise = torch.randn(model_output.shape, dtype=model_output.dtype, device=device, generator=generator).to(
+                device
+            )
+
         prev_sample = prev_sample + noise * sigma_up
 
         if not return_dict:

src/diffusers/schedulers/scheduling_euler_discrete.py

@@ -214,7 +214,16 @@ class EulerDiscreteScheduler(SchedulerMixin, ConfigMixin):
         gamma = min(s_churn / (len(self.sigmas) - 1), 2**0.5 - 1) if s_tmin <= sigma <= s_tmax else 0.0
 
         device = model_output.device if torch.is_tensor(model_output) else "cpu"
-        noise = torch.randn(model_output.shape, dtype=model_output.dtype, generator=generator).to(device)
+        if str(device) == "mps":
+            # randn does not work reproducibly on mps
+            noise = torch.randn(model_output.shape, dtype=model_output.dtype, device="cpu", generator=generator).to(
+                device
+            )
+        else:
+            noise = torch.randn(model_output.shape, dtype=model_output.dtype, device=device, generator=generator).to(
+                device
+            )
+
         eps = noise * s_noise
         sigma_hat = sigma * (gamma + 1)