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🧠 AI🟢 BullishImportance 6/10
MeanCache: From Instantaneous to Average Velocity for Accelerating Flow Matching Inference
arXiv – CS AI|Huanlin Gao, Ping Chen, Fuyuan Shi, Ruijia Wu, Li YanTao, Qiang Hui, Yuren You, Ting Lu, Chao Tan, Shaoan Zhao, Zhaoxiang Liu, Fang Zhao, Kai Wang, Shiguo Lian||3 views
🤖AI Summary
MeanCache introduces a training-free caching framework that accelerates Flow Matching inference by using average velocities instead of instantaneous ones. The framework achieves 3.59X to 4.56X acceleration on major AI models like FLUX.1, Qwen-Image, and HunyuanVideo while maintaining superior generation quality compared to existing caching methods.
Key Takeaways
- →MeanCache achieves significant acceleration ratios of 4.12X, 4.56X, and 3.59X on FLUX.1, Qwen-Image, and HunyuanVideo respectively.
- →The framework uses average velocity perspective with cached Jacobian-vector products to reduce trajectory deviations and error accumulation.
- →MeanCache operates as a training-free solution, making it immediately applicable to existing Flow Matching models.
- →The method consistently outperforms state-of-the-art caching baselines in generation quality while providing substantial speed improvements.
- →A trajectory-stability scheduling strategy using Peak-Suppressed Shortest Path optimizes cache timing and stability.
#meancache#flow-matching#ai-inference#model-acceleration#caching#generative-ai#flux#qwen#hunyuan#optimization
Read Original →via arXiv – CS AI
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