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arXiv · 2510.04188

Let Features Decide Their Own Solvers: Hybrid Feature Caching for Diffusion Transformers

Shikang Zheng·Guantao Chen·Qinming Zhou·Yuqi Lin·Lixuan He·Chang Zou·Peiliang Cai·Jiacheng Liu·Linfeng Zhang
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Abstract

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.

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Shikang Zheng, Guantao Chen, Qinming Zhou, Yuqi Lin, Lixuan He, Chang Zou, Peiliang Cai, Jiacheng Liu, Linfeng Zhang. 2025-10-05. Let Features Decide Their Own Solvers: Hybrid Feature Caching for Diffusion Transformers. https://arxiv.org/abs/2510.04188

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