diff --git a/src/diffusers/schedulers/scheduling_euler_discrete.py b/src/diffusers/schedulers/scheduling_euler_discrete.py
index 802ba0f099f9..8da3434fc347 100644
--- a/src/diffusers/schedulers/scheduling_euler_discrete.py
+++ b/src/diffusers/schedulers/scheduling_euler_discrete.py
@@ -219,8 +219,14 @@ def __init__(
         sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
         timesteps = np.linspace(0, num_train_timesteps - 1, num_train_timesteps, dtype=float)[::-1].copy()
 
-        sigmas = torch.from_numpy(sigmas[::-1].copy()).to(dtype=torch.float32)
-        timesteps = torch.from_numpy(timesteps).to(dtype=torch.float32)
+        sigmas = sigmas[::-1].copy()
+
+        if self.use_karras_sigmas:
+            log_sigmas = np.log(sigmas)
+            sigmas = self._convert_to_karras(in_sigmas=sigmas, num_inference_steps=num_train_timesteps)
+            timesteps = np.array([self._sigma_to_t(sigma, log_sigmas) for sigma in sigmas])
+
+        sigmas = torch.from_numpy(sigmas).to(dtype=torch.float32)
 
         # setable values
         self.num_inference_steps = None
@@ -229,7 +235,7 @@ def __init__(
         if timestep_type == "continuous" and prediction_type == "v_prediction":
             self.timesteps = torch.Tensor([0.25 * sigma.log() for sigma in sigmas])
         else:
-            self.timesteps = timesteps
+            self.timesteps = torch.from_numpy(timesteps.astype(np.float32))
 
         self.sigmas = torch.cat([sigmas, torch.zeros(1, device=sigmas.device)])