Repository: huggingface/diffusers
File: src/diffusers/hooks/taylorseer_cache.py (Python)

Code before:
            is_first_update = self.last_update_step is None
            if not is_first_update:
                delta_step = current_step - self.last_update_step
                if delta_step == 0:
                    raise ValueError("Delta step cannot be zero for TaylorSeer update.")

                # Recursive divided differences up to max_order
                for j in range(self.max_order):
                    prev = self.taylor_factors[i].get(j)
                    if prev is None:
                        break
                    new_factors[j + 1] = (new_factors[j] - prev.to(self.taylor_factors_dtype)) / delta_step
            self.taylor_factors[i] = new_factors
        self.last_update_step = current_step

    def predict(self, current_step: int) -> torch.Tensor:
        if self.last_update_step is None:
            raise ValueError("Cannot predict without prior initialization/update.")

        step_offset = current_step - self.last_update_step

        if not self.taylor_factors:
            raise ValueError("Taylor factors empty during prediction.")

        outputs = []
        for i in range(len(self.module_dtypes)):
            taylor_factors = self.taylor_factors[i]
            # Accumulate Taylor series: f(t0 + Δt) ≈ Σ f^{(n)}(t0) * (Δt^n / n!)
            output = torch.zeros_like(taylor_factors[0])
            for order, factor in taylor_factors.items():
                # Note: order starts at 0
                coeff = (step_offset**order) / math.factorial(order)
                output = output + factor * coeff
            outputs.append(output.to(self.module_dtypes[i]))

        return outputs


class TaylorSeerCacheHook(ModelHook):
    _is_stateful = True

    def __init__(
        self,
        module_name: str,
        predict_steps: int,
        warmup_steps: int,
        taylor_factors_dtype: torch.dtype,
        state_manager: StateManager,
        stop_predicts: Optional[int] = None,
        is_skip: bool = False,
    ):

Review comment (suggestion):
This is a little strange to iterate over the dtypes list for indexing. Why don't we iterate over the self.taylor_factors dict? It already track the outputs?

```python
      step_offset = self.current_step - self.last_update_step

      outputs = []
      for i, factors in self.taylor_factors.items():
          # Accumulate Taylor series: f(t0 + Δt) ≈ Σ f^{(n)}(t0) * (Δt^n / n!)
          output = torch.zeros_like(factors[0])
          for order, factor in factors.items():
              coeff = (step_offset ** order) / math.factorial(order)
              output = output + factor * coeff
          outputs.append(output.to(self.output_dtypes[i]))

      return outputs

```

Code after:
        self.inactive_shapes = None
        self.device = None

    def update(
        self,
        outputs: Tuple[torch.Tensor, ...],
    ) -> None:
        self.module_dtypes = tuple(output.dtype for output in outputs)
        self.device = outputs[0].device

        if self.is_inactive:
            self.inactive_shapes = tuple(output.shape for output in outputs)
        else:
            for i, features in enumerate(outputs):
                new_factors: Dict[int, torch.Tensor] = {0: features}
                is_first_update = self.last_update_step is None
                if not is_first_update:
                    delta_step = self.current_step - self.last_update_step
                    if delta_step == 0:
                        raise ValueError("Delta step cannot be zero for TaylorSeer update.")

                    # Recursive divided differences up to max_order
                    prev_factors = self.taylor_factors.get(i, {})
                    for j in range(self.max_order):
                        prev = prev_factors.get(j)
                        if prev is None:
                            break
                        new_factors[j + 1] = (new_factors[j] - prev.to(features.dtype)) / delta_step
                self.taylor_factors[i] = {
                    order: factor.to(self.taylor_factors_dtype) for order, factor in new_factors.items()
                }

        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.")

        step_offset = self.current_step - self.last_update_step

        outputs = []
        if self.is_inactive:
            if self.inactive_shapes is None:
                raise ValueError("Inactive shapes not set during prediction.")
            for i in range(len(self.module_dtypes)):
                outputs.append(
                    torch.zeros(
                        self.inactive_shapes[i],
                        dtype=self.module_dtypes[i],
                        device=self.device,