SeaCache: Spectral-Evolution-Aware Cache for Accelerating Diffusion Models
Diffusion models are a strong backbone for visual generation, but their inherently sequential denoising process leads to slow inference. Previous methods accelerate sampling by caching and reusing intermediate outputs based on feature distances between adjacent timesteps. However, existing caching strategies typically rely on raw feature differences that entangle content and noise. This design overlooks spectral evolution, where low-frequency structure appears early and high-frequency detail is refined later. We introduce Spectral-Evolution-Aware Cache (SeaCache), a training-free cache schedule that bases reuse decisions on a spectrally aligned representation. Through theoretical and empirical analysis, we derive a Spectral-Evolution-Aware (SEA) filter that preserves content-relevant components while suppressing noise. Employing SEA-filtered input features to estimate redundancy leads to dynamic schedules that adapt to content while respecting the spectral priors underlying the diffusion model. Extensive experiments on diverse visual generative models and the baselines show that SeaCache achieves state-of-the-art latency-quality trade-offs.
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Jiwoo Chung
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2026-02-26T10:01:33.083Z
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jiwoo chung
wldn0202
user
wldn0202
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Sangeek Hyun
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MinKyu Lee
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2026-03-03T10:43:06.794Z
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MinKyu Lee
MinKyLee
user
MinKyLee
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Byeongju Han
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Geonho Cha
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Dongyoon Wee
699febcaebfce7fbcca91c45
Youngjun Hong
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Jae-Pil Heo
SeaCache: Spectral-Evolution-Aware Cache for Accelerating Diffusion Models
Diffusion models are a strong backbone for visual generation, but their inherently sequential denoising process leads to slow inference. Previous methods accelerate sampling by caching and reusing intermediate outputs based on feature distances between adjacent timesteps. However, existing caching strategies typically rely on raw feature differences that entangle content and noise. This design overlooks spectral evolution, where low-frequency structure appears early and high-frequency detail is refined later. We introduce Spectral-Evolution-Aware Cache (SeaCache), a training-free cache schedule that bases reuse decisions on a spectrally aligned representation. Through theoretical and empirical analysis, we derive a Spectral-Evolution-Aware (SEA) filter that preserves content-relevant components while suppressing noise. Employing SEA-filtered input features to estimate redundancy leads to dynamic schedules that adapt to content while respecting the spectral priors underlying the diffusion model. Extensive experiments on diverse visual generative models and the baselines show that SeaCache achieves state-of-the-art latency-quality trade-offs.
Spectral-Evolution-Aware Cache (SeaCache) improves diffusion model inference speed by using spectrally aligned representations to optimize intermediate output reuse, achieving better latency-quality trade-offs than previous methods.
diffusion models denoising process caching strategies feature distances spectral evolution spectrally aligned representation Spectral-Evolution-Aware filter redundancy estimation dynamic schedules latency-quality trade-offs