BeCARE: Budgeted Cache Refresh for Diffusion Transformer Acceleration
Training-free feature caching accelerates diffusion transformer (DiT) inference by reusing or forecasting intermediate features. However, fixed schedules make compute predictable but ignore prompt- and timestep-dependent risk, whereas hand-tuned error thresholds adapt locally but leave realized compute difficult to control. To address these limitations, we present Budgeted Cache Refresh (BeCARE), a training-free framework that, given a user-specified cap on full computations, adaptively determines when to refresh the cache during accelerated inference. Specifically, we first derive an error-amplification profile from the sampler's noise schedule to characterize the varying impact of appro...
| Authors | Yuhang Zhang, Junxiang Qiu, Huixia Ben et al. |
|---|---|
| Primary category | cs.CV |
| Published | 26 May 2026 |
| Last updated at source | 29 July 2026 |
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