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[2508.12691] Adaptive Hybrid Caching for Efficient Text-to-Video Diffusion Model Acceleration
Summary
This paper presents MixCache, a novel caching framework designed to enhance the efficiency of text-to-video diffusion models, significantly improving generation speed and quality.
Why It Matters
As demand for high-quality video generation increases, optimizing computational efficiency becomes crucial. MixCache addresses the limitations of existing caching methods by introducing a hybrid strategy that balances speed and quality, making it relevant for researchers and developers in AI and multimedia fields.
Key Takeaways
- MixCache offers a training-free approach to caching in video generation models.
- It uses a context-aware strategy to optimize when caching is applied.
- The framework provides significant speed improvements (up to 1.97x) without sacrificing quality.
- MixCache distinguishes between different caching strategies for better performance.
- The research highlights the importance of balancing inference speed and generation quality in AI models.
Computer Science > Graphics arXiv:2508.12691 (cs) [Submitted on 18 Aug 2025 (v1), last revised 26 Feb 2026 (this version, v2)] Title:Adaptive Hybrid Caching for Efficient Text-to-Video Diffusion Model Acceleration Authors:Yuanxin Wei, Lansong Diao, Bujiao Chen, Shenggan Cheng, Zhengping Qian, Wenyuan Yu, Nong Xiao, Wei Lin, Jiangsu Du View a PDF of the paper titled Adaptive Hybrid Caching for Efficient Text-to-Video Diffusion Model Acceleration, by Yuanxin Wei and Lansong Diao and Bujiao Chen and Shenggan Cheng and Zhengping Qian and Wenyuan Yu and Nong Xiao and Wei Lin and Jiangsu Du View PDF HTML (experimental) Abstract:Efficient video generation models are increasingly vital for multimedia synthetic content generation. Leveraging the Transformer architecture and the diffusion process, video DiT models have emerged as a dominant approach for high-quality video generation. However, their multi-step iterative denoising process incurs high computational cost and inference latency. Caching, a widely adopted optimization method in DiT models, leverages the redundancy in the diffusion process to skip computations in different granularities (e.g., step, cfg, block). Nevertheless, existing caching methods are limited to single-granularity strategies, struggling to balance generation quality and inference speed in a flexible manner. In this work, we propose MixCache, a training-free caching-based framework for efficient video DiT inference. It first distinguishes the interference...
