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

• URL: http://arxiv.org/abs/2510.04188v1
• Date: Sun, 05 Oct 2025 13:01:08 GMT
• Title: Let Features Decide Their Own Solvers: Hybrid Feature Caching for Diffusion Transformers
• Authors: Shikang Zheng, Guantao Chen, Qinming Zhou, Yuqi Lin, Lixuan He, Chang Zou, Peiliang Cai, Jiacheng Liu, Linfeng Zhang,
• Abstract summary: Diffusion Transformers offer state-of-the-art fidelity in image and video synthesis, but their iterative sampling process remains a major bottleneck.<n>To mitigate this, feature caching has emerged as a training-free acceleration technique that reuses or forecasts hidden representations.<n>We introduce HyCa, a Hybrid ODE solver inspired caching framework that applies dimension-wise caching strategies.
• Score: 10.215762814937277
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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