diff --git a/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py b/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py
index 9c15df4569ca..051275ad946c 100644
--- a/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py
+++ b/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py
@@ -442,15 +442,13 @@ 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()
+            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()
+            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)
@@ -467,6 +465,16 @@ def set_timesteps(
         else:
             sigmas = np.interp(timesteps, np.arange(0, len(sigmas)), sigmas)
 
+        # When using karras or lu sigmas with certain beta schedules (e.g. squaredcos_cap_v2),
+        # the sigma-to-timestep mapping can produce duplicate integer timesteps. Deduplicate
+        # them to prevent the step index from drifting out of bounds during multistep updates.
+        timesteps_int = np.round(timesteps).astype(np.int64)
+        _, unique_indices = np.unique(timesteps_int, return_index=True)
+        if len(unique_indices) < len(timesteps_int):
+            unique_indices = np.sort(unique_indices)
+            timesteps = timesteps[unique_indices]
+            sigmas = sigmas[unique_indices]
+
         if self.config.final_sigmas_type == "sigma_min":
             sigma_last = ((1 - self.alphas_cumprod[0]) / self.alphas_cumprod[0]) ** 0.5
         elif self.config.final_sigmas_type == "zero":