[2603.01623] Adaptive Spectral Feature Forecasting for Diffusion Sampling Acceleration

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.01623 (cs) [Submitted on 2 Mar 2026] Title:Adaptive Spectral Feature Forecasting for Diffusion Sampling Acceleration Authors:Jiaqi Han, Juntong Shi, Puheng Li, Haotian Ye, Qiushan Guo, Stefano Ermon View a PDF of the paper titled Adaptive Spectral Feature Forecasting for Diffusion Sampling Acceleration, by Jiaqi Han and 5 other authors View PDF HTML (experimental) Abstract:Diffusion models have become the dominant tool for high-fidelity image and video generation, yet are critically bottlenecked by their inference speed due to the numerous iterative passes of Diffusion Transformers. To reduce the exhaustive compute, recent works resort to the feature caching and reusing scheme that skips network evaluations at selected diffusion steps by using cached features in previous steps. However, their preliminary design solely relies on local approximation, causing errors to grow rapidly with large skips and leading to degraded sample quality at high speedups. In this work, we propose spectral diffusion feature forecaster (Spectrum), a training-free approach that enables global, long-range feature reuse with tightly controlled error. In particular, we view the latent features of the denoiser as functions over time and approximate them with Chebyshev polynomials. Specifically, we fit the coefficient for each basis via ridge regression, which is then leveraged to forecast features at multiple future diffusion steps...