diff --git a/src/diffusers/hooks/taylorseer_cache.py b/src/diffusers/hooks/taylorseer_cache.py
index 607d652f4a..bbca345e8b 100644
--- a/src/diffusers/hooks/taylorseer_cache.py
+++ b/src/diffusers/hooks/taylorseer_cache.py
@@ -157,6 +157,7 @@ class TaylorSeerState:
 
         self.last_update_step = self.current_step
 
+    @torch.compiler.disable
     def predict(self) -> List[torch.Tensor]:
         if self.last_update_step is None:
             raise ValueError("Cannot predict without prior initialization/update.")
@@ -219,7 +220,8 @@ class TaylorSeerCacheHook(ModelHook):
         """
         self.state_manager.reset()
 
-    def new_forward(self, module: torch.nn.Module, *args, **kwargs):
+    @torch.compiler.disable
+    def _measure_should_compute(self) -> bool:
         state: TaylorSeerState = self.state_manager.get_state()
         state.current_step += 1
         current_step = state.current_step
@@ -227,6 +229,10 @@ class TaylorSeerCacheHook(ModelHook):
         is_compute_interval = (current_step - self.disable_cache_before_step - 1) % self.cache_interval == 0
         is_cooldown_phase = self.disable_cache_after_step is not None and current_step >= self.disable_cache_after_step
         should_compute = is_warmup_phase or is_compute_interval or is_cooldown_phase
+        return should_compute, state
+
+    def new_forward(self, module: torch.nn.Module, *args, **kwargs):
+        should_compute, state = self._measure_should_compute()
         if should_compute:
             outputs = self.fn_ref.original_forward(*args, **kwargs)
             wrapped_outputs = (outputs,) if isinstance(outputs, torch.Tensor) else outputs