ARCS: APPROXIMATION-RISK-AWARE COMPUTATION SCHEDULING FOR FEATURE FORECASTING IN DIFFUSION TRANSFORMERS

Diffusion Transformers achieve strong generation quality but require expensive network evaluations across many denoising timesteps. Feature caching reduces this cost by reusing intermediate activations at subsequent timesteps. Recent methods mathematically approximate the temporal evolution of cached features to forecast their values, reducing errors associated with direct reuse. However, these forecasting methods often rely on predefined computation schedules, implicitly assuming that forecasts remain reliable for a predetermined duration. In practice, forecasting reliability varies substantially throughout denoising, making when to compute as important as how much to compute. We propose Approximation-Risk-Aware Computation Scheduling (ARCS), a training-free method that adaptively allocates full computations according to estimated forecasting risk. We instantiate ARCS for Taylor feature forecasting, where adaptive computation produces irregularly spaced computation anchors. To handle these irregular anchors, we formulate Taylor forecasting with Newton divided differences and derive non-uniform approximation-error proxies that provide inexpensive online risk signals for determining when full computation is needed. Across image and video generation models, ARCS improves generation quality over fixed-interval Taylor forecasting, with the largest gains observed under aggressive DiT acceleration. In particular, ARCS reduces FID and sFID by up to 18.2% and 19.8%, respectively, compared with fixed-interval Taylor forecasting at matched full-computation budgets.