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arXiv · 2508.08978

TaoCache: Structure-Maintained Video Generation Acceleration

Zhentao Fan·Zongzuo Wang·Weiwei Zhang
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Abstract

Existing cache-based acceleration methods for video diffusion models primarily skip early or mid denoising steps, which often leads to structural discrepancies relative to full-timestep generation and can hinder instruction following and character consistency. We present TaoCache, a training-free, plug-and-play caching strategy that, instead of residual-based caching, adopts a fixed-point perspective to predict the model's noise output and is specifically effective in late denoising stages. By calibrating cosine similarities and norm ratios of consecutive noise deltas, TaoCache preserves high-resolution structure while enabling aggressive skipping. The approach is orthogonal to complementary accelerations such as Pyramid Attention Broadcast (PAB) and TeaCache, and it integrates seamlessly into DiT-based frameworks. Across Latte-1, OpenSora-Plan v110, and Wan2.1, TaoCache attains substantially higher visual quality (LPIPS, SSIM, PSNR) than prior caching methods under the same speedups.

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Zhentao Fan, Zongzuo Wang, Weiwei Zhang. 2025-08-12. TaoCache: Structure-Maintained Video Generation Acceleration. https://arxiv.org/abs/2508.08978

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