diff --git a/src/diffusers/schedulers/scheduling_dpmsolver_singlestep.py b/src/diffusers/schedulers/scheduling_dpmsolver_singlestep.py
index 1d34977d4a57..9307db89d8d7 100644
--- a/src/diffusers/schedulers/scheduling_dpmsolver_singlestep.py
+++ b/src/diffusers/schedulers/scheduling_dpmsolver_singlestep.py
@@ -241,7 +241,7 @@ def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.devic
         self.num_inference_steps = num_inference_steps
         # Clipping the minimum of all lambda(t) for numerical stability.
         # This is critical for cosine (squaredcos_cap_v2) noise schedule.
-        clipped_idx = torch.searchsorted(torch.flip(self.lambda_t, [0]), self.lambda_min_clipped)
+        clipped_idx = torch.searchsorted(torch.flip(self.lambda_t, [0]), self.config.lambda_min_clipped)
         timesteps = (
             np.linspace(0, self.config.num_train_timesteps - 1 - clipped_idx, num_inference_steps + 1)
             .round()[::-1][:-1]