LeMiCa通过优化路径误差，让视频生成更快更一致。
LeMiCa: Lexicographic Minimax Path Caching for Efficient Diffusion-Based Video Generation
- 构建带误差权重的图结构，用字典序极小化路径优化缓存顺序。
- 在Latte模型上提速2.9倍，Open-Sora上LPIPS达0.05。
- 无需训练，适用于多种扩散视频生成场景，适合追求高效可靠的开发者。
我们提出LeMiCa，一种无需训练的高效加速框架，用于扩散模型驱动的视频生成。现有缓存策略多关注局部启发式误差，却常忽略全局误差累积，导致加速后视频内容明显退化。为此，我们将缓存调度建模为带误差权重的有向图，并引入字典序极小化路径优化策略，显式约束最坏路径误差。该方法显著提升生成帧间全局内容与风格的一致性。在多个文本到视频基准上的实验表明，LeMiCa同时实现推理速度与生成质量的双重提升：在Latte模型上实现2.9倍加速，在Open-Sora上达到0.05的LPIPS得分，优于先前缓存技术。这些增益伴随极低感知质量损失，使LeMiCa成为扩散视频生成加速的鲁棒通用范式。代码已开源。
原文摘要 · Abstract (English)
We present LeMiCa, a training-free and efficient acceleration framework for diffusion-based video generation. While existing caching strategies primarily focus on reducing local heuristic errors, they often overlook the accumulation of global errors, leading to noticeable content degradation between accelerated and original videos. To address this issue, we formulate cache scheduling as a directed graph with error-weighted edges and introduce a Lexicographic Minimax Path Optimization strategy that explicitly bounds the worst-case path error. This approach substantially improves the consistency of global content and style across generated frames. Extensive experiments on multiple text-to-video benchmarks demonstrate that LeMiCa delivers dual improvements in both inference speed and generation quality. Notably, our method achieves a 2.9x speedup on the Latte model and reaches an LPIPS score of 0.05 on Open-Sora, outperforming prior caching techniques. Importantly, these gains come with minimal perceptual quality degradation, making LeMiCa a robust and generalizable paradigm for accelerating diffusion-based video generation. We believe this approach can serve as a strong foundation for future research on efficient and reliable video synthesis. Our code is available at :https://github.com/UnicomAI/LeMiCa
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