diff --git a/src/diffusers/schedulers/scheduling_ddim.py b/src/diffusers/schedulers/scheduling_ddim.py
index 2dc85a93adc9..2d24ecac1d95 100644
--- a/src/diffusers/schedulers/scheduling_ddim.py
+++ b/src/diffusers/schedulers/scheduling_ddim.py
@@ -300,11 +300,9 @@ def add_noise(
         noise: torch.FloatTensor,
         timesteps: torch.IntTensor,
     ) -> torch.FloatTensor:
-        if self.alphas_cumprod.device != original_samples.device:
-            self.alphas_cumprod = self.alphas_cumprod.to(original_samples.device)
-
-        if timesteps.device != original_samples.device:
-            timesteps = timesteps.to(original_samples.device)
+        # Make sure alphas_cumprod and timestep have same device and dtype as original_samples
+        self.alphas_cumprod = self.alphas_cumprod.to(device=original_samples.device, dtype=original_samples.dtype)
+        timesteps = timesteps.to(original_samples.device)
 
         sqrt_alpha_prod = self.alphas_cumprod[timesteps] ** 0.5
         sqrt_alpha_prod = sqrt_alpha_prod.flatten()
diff --git a/src/diffusers/schedulers/scheduling_ddpm.py b/src/diffusers/schedulers/scheduling_ddpm.py
index e1db9079d149..77ed98137708 100644
--- a/src/diffusers/schedulers/scheduling_ddpm.py
+++ b/src/diffusers/schedulers/scheduling_ddpm.py
@@ -294,11 +294,9 @@ def add_noise(
         noise: torch.FloatTensor,
         timesteps: torch.IntTensor,
     ) -> torch.FloatTensor:
-        if self.alphas_cumprod.device != original_samples.device:
-            self.alphas_cumprod = self.alphas_cumprod.to(original_samples.device)
-
-        if timesteps.device != original_samples.device:
-            timesteps = timesteps.to(original_samples.device)
+        # Make sure alphas_cumprod and timestep have same device and dtype as original_samples
+        self.alphas_cumprod = self.alphas_cumprod.to(device=original_samples.device, dtype=original_samples.dtype)
+        timesteps = timesteps.to(original_samples.device)
 
         sqrt_alpha_prod = self.alphas_cumprod[timesteps] ** 0.5
         sqrt_alpha_prod = sqrt_alpha_prod.flatten()
diff --git a/src/diffusers/schedulers/scheduling_lms_discrete.py b/src/diffusers/schedulers/scheduling_lms_discrete.py
index 802da468cda6..1f6187c727c9 100644
--- a/src/diffusers/schedulers/scheduling_lms_discrete.py
+++ b/src/diffusers/schedulers/scheduling_lms_discrete.py
@@ -257,9 +257,13 @@ def add_noise(
         noise: torch.FloatTensor,
         timesteps: torch.FloatTensor,
     ) -> torch.FloatTensor:
-        sigmas = self.sigmas.to(original_samples.device)
-        schedule_timesteps = self.timesteps.to(original_samples.device)
+        # 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)
         timesteps = timesteps.to(original_samples.device)
+
+        schedule_timesteps = self.timesteps
+
         if isinstance(timesteps, torch.IntTensor) or isinstance(timesteps, torch.LongTensor):
             deprecate(
                 "timesteps as indices",
@@ -273,7 +277,7 @@ def add_noise(
         else:
             step_indices = [(schedule_timesteps == t).nonzero().item() for t in timesteps]
 
-        sigma = sigmas[step_indices].flatten()
+        sigma = self.sigmas[step_indices].flatten()
         while len(sigma.shape) < len(original_samples.shape):
             sigma = sigma.unsqueeze(-1)
 
diff --git a/src/diffusers/schedulers/scheduling_pndm.py b/src/diffusers/schedulers/scheduling_pndm.py
index f6a6d6153be5..b29712e1e736 100644
--- a/src/diffusers/schedulers/scheduling_pndm.py
+++ b/src/diffusers/schedulers/scheduling_pndm.py
@@ -400,11 +400,9 @@ def add_noise(
         noise: torch.FloatTensor,
         timesteps: torch.IntTensor,
     ) -> torch.Tensor:
-        if self.alphas_cumprod.device != original_samples.device:
-            self.alphas_cumprod = self.alphas_cumprod.to(original_samples.device)
-
-        if timesteps.device != original_samples.device:
-            timesteps = timesteps.to(original_samples.device)
+        # Make sure alphas_cumprod and timestep have same device and dtype as original_samples
+        self.alphas_cumprod = self.alphas_cumprod.to(device=original_samples.device, dtype=original_samples.dtype)
+        timesteps = timesteps.to(original_samples.device)
 
         sqrt_alpha_prod = self.alphas_cumprod[timesteps] ** 0.5
         sqrt_alpha_prod = sqrt_alpha_prod.flatten()