Accelerating Frequency Domain Diffusion Models with Error-Feedback Event-Driven Caching
Diffusion models achieve remarkable success in time series generation. However, slow inference limits their practical deployment. We propose E$^2$-CRF (Error-Feedback Event-Driven Cumulative Residual Feature caching) to accelerate frequency domain diffusion models. Our method exploits two structural properties: (1) spectral localization, where signal energy concentrates in low frequencies, and (2) mirror symmetry, which halves the effective frequency dimension. E$^2$-CRF uses a closed-loop error-feedback system that adaptively caches transformer KV features across diffusion steps. We trigger recomputation using event-driven residual dynamics instead of fixed schedules. Our method selectiv...
| Authors | Dong Liu, Yanxuan Yu, Ying Nian Wu |
|---|---|
| Primary category | cs.LG |
| Published | 24 April 2026 |
| Last updated at source | 26 July 2026 |
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