Kai-Liang Cheng

Hybrid-Basis Feature Forecasting for Diffusion Sampling Acceleration

We propose Hybrid-Basis Feature Forecasting (HybridFF), a training-free, plug-and-play framework for accelerating diffusion sampling. To capture local smoothness, long-range trends, and complex non-monotonic variations when modeling feature evolution, HybridFF first estimates coefficients using moving least squares (ML...