TaoCache: Structure-Maintained Video Generation Acceleration

• URL: http://arxiv.org/abs/2508.08978v1
• Date: Tue, 12 Aug 2025 14:40:36 GMT
• Title: TaoCache: Structure-Maintained Video Generation Acceleration
• Authors: Zhentao Fan, Zongzuo Wang, Weiwei Zhang,
• Abstract summary: We present TaoCache, a training-free, plug-and-play caching strategy for video diffusion models.<n>It adopts a fixed-point perspective to predict the model's noise output and is specifically effective in late denoising stages.
• Score: 4.594224594572109
• License: http://creativecommons.org/licenses/by/4.0/
• 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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