[2603.03792] TAP: A Token-Adaptive Predictor Framework for Training-Free Diffusion Acceleration

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.03792 (cs) [Submitted on 4 Mar 2026] Title:TAP: A Token-Adaptive Predictor Framework for Training-Free Diffusion Acceleration Authors:Haowei Zhu, Tingxuan Huang, Xing Wang, Tianyu Zhao, Jiexi Wang, Weifeng Chen, Xurui Peng, Fangmin Chen, Junhai Yong, Bin Wang View a PDF of the paper titled TAP: A Token-Adaptive Predictor Framework for Training-Free Diffusion Acceleration, by Haowei Zhu and 9 other authors View PDF HTML (experimental) Abstract:Diffusion models achieve strong generative performance but remain slow at inference due to the need for repeated full-model denoising passes. We present Token-Adaptive Predictor (TAP), a training-free, probe-driven framework that adaptively selects a predictor for each token at every sampling step. TAP uses a single full evaluation of the model's first layer as a low-cost probe to compute proxy losses for a compact family of candidate predictors (instantiated primarily with Taylor expansions of varying order and horizon), then assigns each token the predictor with the smallest proxy error. This per-token "probe-then-select" strategy exploits heterogeneous temporal dynamics, requires no additional training, and is compatible with various predictor designs. TAP incurs negligible overhead while enabling large speedups with little or no perceptual quality loss. Extensive experiments across multiple diffusion architectures and generation tasks show that TAP substantially...