EpaCache: Error-Propagation-Aware Caching for Accelerating Diffusion-Based Visual Generation

• URL: http://arxiv.org/abs/2608.29264v1
• Date: Sat, 29 Aug 2026 13:38:11 GMT
• Title: EpaCache: Error-Propagation-Aware Caching for Accelerating Diffusion-Based Visual Generation
• Abstract summary: Caching-based methods reduce inference latency by reusing intermediate computations across adjacent timesteps.<n>EpaCache is a training-free caching policy that adaptively allocates the reuse budget on timesteps with lower downstream impact.<n>EpaCache consistently improves the latency--fidelity trade-off over existing caching methods.
• Score: 30.438361624564553
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Diffusion-based visual generative models deliver strong image and video synthesis quality but incur high inference costs because sequential samplers repeatedly evaluate large networks. Caching-based methods reduce inference latency by reusing intermediate computations across adjacent timesteps. However, existing cache controllers rely primarily on local temporal variation and overlook the trajectory-level consequences of cache reuse. We introduce Error-Propagation-Aware Cache (EpaCache), a training-free caching policy that adaptively allocates the reuse budget on timesteps with lower downstream impact. Experiments on image and video synthesis models demonstrate that EpaCache consistently improves the latency--fidelity trade-off over existing caching methods. On FLUX.1-dev, EpaCache outperforms the prior state-of-the-art caching method in both latency and fidelity, reducing inference time from $11.7$ s to $11.3$ s while improving PSNR from $21.4$ to $22.8$. On HunyuanVideo, EpaCache achieves a $2.63\times$ speedup over uncached inference and improves SSIM from $0.891$ to $0.905$ over the prior state-of-the-art method at matched latency.

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