diff --git a/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py b/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py
index 9c15df4569ca..8f5ae1b04620 100644
--- a/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py
+++ b/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py
@@ -442,15 +442,11 @@ def set_timesteps(
             sigmas = np.flip(sigmas).copy()
             sigmas = self._convert_to_karras(in_sigmas=sigmas, num_inference_steps=num_inference_steps)
             timesteps = np.array([self._sigma_to_t(sigma, log_sigmas) for sigma in sigmas])
-            if self.config.beta_schedule != "squaredcos_cap_v2":
-                timesteps = timesteps.round()
         elif self.config.use_lu_lambdas:
             lambdas = np.flip(log_sigmas.copy())
             lambdas = self._convert_to_lu(in_lambdas=lambdas, num_inference_steps=num_inference_steps)
             sigmas = np.exp(lambdas)
             timesteps = np.array([self._sigma_to_t(sigma, log_sigmas) for sigma in sigmas])
-            if self.config.beta_schedule != "squaredcos_cap_v2":
-                timesteps = timesteps.round()
         elif self.config.use_exponential_sigmas:
             sigmas = np.flip(sigmas).copy()
             sigmas = self._convert_to_exponential(in_sigmas=sigmas, num_inference_steps=num_inference_steps)
@@ -476,6 +472,17 @@ def set_timesteps(
                 f"`final_sigmas_type` must be one of 'zero', or 'sigma_min', but got {self.config.final_sigmas_type}"
             )
 
+        # When sigma-to-timestep mapping produces duplicates after rounding to integer
+        # (e.g. cosine schedule with karras/lu sigmas where many sigmas map to nearly the same timestep),
+        # deduplicate while preserving order to prevent index out-of-bounds errors in multistep solvers.
+        # This mirrors the same fix in DPMSolverMultistepInverseScheduler.
+        timesteps = np.round(timesteps).astype(np.int64)
+        _, unique_indices = np.unique(timesteps, return_index=True)
+        unique_indices = np.sort(unique_indices)
+        if len(unique_indices) < len(timesteps):
+            timesteps = timesteps[unique_indices]
+            sigmas = sigmas[unique_indices]
+
         sigmas = np.concatenate([sigmas, [sigma_last]]).astype(np.float32)
 
         self.sigmas = torch.from_numpy(sigmas)