diff --git a/src/diffusers/schedulers/scheduling_k_dpm_2_ancestral_discrete.py b/src/diffusers/schedulers/scheduling_k_dpm_2_ancestral_discrete.py
index 175f338b92..711bdf2d5e 100644
--- a/src/diffusers/schedulers/scheduling_k_dpm_2_ancestral_discrete.py
+++ b/src/diffusers/schedulers/scheduling_k_dpm_2_ancestral_discrete.py
@@ -161,16 +161,16 @@ def set_timesteps(
         # standard deviation of the initial noise distribution
         self.init_noise_sigma = self.sigmas.max()
 
-        timesteps = torch.from_numpy(timesteps).to(device)
-        timesteps_interpol = self.sigma_to_t(sigmas_interpol).to(device)
-        interleaved_timesteps = torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None]), dim=-1).flatten()
-        timesteps = torch.cat([timesteps[:1], interleaved_timesteps])
-
         if str(device).startswith("mps"):
             # mps does not support float64
-            self.timesteps = timesteps.to(device, dtype=torch.float32)
+            timesteps = torch.from_numpy(timesteps).to(device, dtype=torch.float32)
         else:
-            self.timesteps = timesteps
+            timesteps = torch.from_numpy(timesteps).to(device)
+
+        timesteps_interpol = self.sigma_to_t(sigmas_interpol).to(device)
+        interleaved_timesteps = torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None]), dim=-1).flatten()
+
+        self.timesteps = torch.cat([timesteps[:1], interleaved_timesteps])
 
         self.sample = None
 
diff --git a/src/diffusers/schedulers/scheduling_k_dpm_2_discrete.py b/src/diffusers/schedulers/scheduling_k_dpm_2_discrete.py
index 18dd976716..a46cc06052 100644
--- a/src/diffusers/schedulers/scheduling_k_dpm_2_discrete.py
+++ b/src/diffusers/schedulers/scheduling_k_dpm_2_discrete.py
@@ -149,18 +149,17 @@ def set_timesteps(
         # standard deviation of the initial noise distribution
         self.init_noise_sigma = self.sigmas.max()
 
-        timesteps = torch.from_numpy(timesteps).to(device)
+        if str(device).startswith("mps"):
+            # mps does not support float64
+            timesteps = torch.from_numpy(timesteps).to(device, dtype=torch.float32)
+        else:
+            timesteps = torch.from_numpy(timesteps).to(device)
 
         # interpolate timesteps
         timesteps_interpol = self.sigma_to_t(sigmas_interpol).to(device)
         interleaved_timesteps = torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None]), dim=-1).flatten()
-        timesteps = torch.cat([timesteps[:1], interleaved_timesteps])
 
-        if str(device).startswith("mps"):
-            # mps does not support float64
-            self.timesteps = timesteps.to(torch.float32)
-        else:
-            self.timesteps = timesteps
+        self.timesteps = torch.cat([timesteps[:1], interleaved_timesteps])
 
         self.sample = None