Diffusion Transformers offer state-of-the-art fidelity in image and video synthesis, but their iterative sampling process remains a major bottleneck due to the high cost of transformer forward passes at each timestep. To mitigate this, feature caching has emerged as a training-free acceleration technique that reuses or forecasts hidden representations. However, existing methods often apply a uniform caching strategy across all feature dimensions, ignoring their heterogeneous dynamic behaviors. Therefore, we adopt a new perspective by modeling hidden feature evolution as a mixture of ODEs across dimensions, and introduce HyCa, a Hybrid ODE solver inspired caching framework that applies dimension-wise caching strategies. HyCa achieves near-lossless acceleration across diverse domains and models, including 5.55 times speedup on FLUX, 5.56 times speedup on HunyuanVideo, 6.24 times speedup on Qwen-Image and Qwen-Image-Edit without retraining.
@article{arxiv.2510.04188,
title = {Let Features Decide Their Own Solvers: Hybrid Feature Caching for Diffusion Transformers},
author = {Shikang Zheng and Guantao Chen and Qinming Zhou and Yuqi Lin and Lixuan He and Chang Zou and Peiliang Cai and Jiacheng Liu and Linfeng Zhang},
journal= {arXiv preprint arXiv:2510.04188},
year = {2025}
}