Fix `use_lu_lambdas` and `use_karras_sigmas` with...

Fix `use_lu_lambdas` and `use_karras_sigmas` with `beta_schedule=squaredcos_cap_v2` in `DPMSolverMultistepScheduler` (#10740)

src/diffusers/schedulers/scheduling_dpmsolver_multistep.py

@@ -399,12 +399,16 @@ class DPMSolverMultistepScheduler(SchedulerMixin, ConfigMixin):
         if self.config.use_karras_sigmas:
             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]).round()
+            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]).round()
+            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)