FlexCache通过压缩与分体缓存，显著提升视频扩散模型推理效率
FlexCache: Flexible Approximate Cache System for Video Diffusion
- 压缩缓存数据并分离物体与背景，降低存储开销
- 平均缓存消耗减少6.7倍，吞吐量提升1.26倍
- 适合大规模视频生成场景，尤其对资源受限部署有帮助
文本到视频应用受到广泛关注，其中扩散模型因生成质量优异成为主流。然而，其计算复杂度高，单视频生成常需数分钟。现有针对文本到图像扩散模型的优化技术难以直接应用于视频生成，因视频所需缓存更大、计算更密集。本文提出 FlexCache，一种灵活近似缓存系统，包含两项核心设计：首先，压缩缓存数据后存储，平均可减少6.7倍存储消耗；其次，通过解耦物体与背景，提升缓存命中率与计算节省。此外，设计专用缓存替换策略以更好支持上述技术。评估表明，相比当前最优近似缓存系统，FlexCache实现1.26倍更高的吞吐量和25%更低的成本。
原文摘要 · Abstract (English)
Text-to-Video applications receive increasing attention from the public. Among these, diffusion models have emerged as the most prominent approach, offering impressive quality in visual content generation. However, it still suffers from substantial computational complexity, often requiring several minutes to generate a single video. While prior research has addressed the computational overhead in text-to-image diffusion models, the techniques developed are not directly suitable for video diffusion models due to the significantly larger cache requirements and enhanced computational demands associated with video generation. We present FlexCache, a flexible approximate cache system that addresses the challenges in two main designs. First, we compress the caches before saving them to storage. Our compression strategy can reduce 6.7 times consumption on average. Then we find that the approximate cache system can achieve higher hit rate and computation savings by decoupling the object and background. We further design a tailored cache replacement policy to support the two techniques mentioned above better. Through our evaluation, FlexCache reaches 1.26 times higher throughput and 25% lower cost compared to the state-of-the-art diffusion approximate cache system.
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