MixCache: Mixture-of-Cache for Video Diffusion Transformer Acceleration

Y uanxin Wei 1 Lansong Diao 2 Bujiao Chen 2 Shenggan Cheng 3 Zhengping Qian 2 Wenyuan Y u 2 Nong Xiao 1 Wei Lin 2 Jiangsu Du 1 1 Sun Y at-sen University 2 Alibaba Group 3 National University of Singapore Corresponding authors Abstract Leveraging the Transformer architecture and the diffusion process, video DiT models have emerged as a dominant approach for high-quality video generation. 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 Mix-Cache, a training-free caching-based framework for efficient video DiT inference. It first distinguishes the interference and boundary between different caching strategies, and then introduces a context-aware cache triggering strategy to determine when caching should be enabled, along with an adaptive hybrid cache decision strategy for dynamically selecting the optimal caching granularity. Extensive experiments on diverse models demonstrate that, MixCache can significantly accelerate video generation (e.g., 1.94 speedup on Wan 14B, 1.97 speedup on HunyuanVideo) while delivering both superior generation quality and inference efficiency compared to baseline methods. 1 Introduction Diffusion Transformer (DiT) (Peebles and Xie 2023) has revolutionized video generation by integrating the scalability of the Transformer architecture (V aswani et al. 2017) with the power of the diffusion process (Ho, Jain, and Abbeel 2020; Rombach et al. 2022), enabling unprecedented quality in video generation. Cutting-edge video DiT models emerge including SD3.0 (Esser et al. 2024), Sora (Brooks et al. 2024), CogVideoX (Y ang et al. 2024b) and Wan (Wang et al. 2025). These models have facilitated the development of many meaningful applications, such as text-to-video generation (Khachatryan et al. 2023; Wu et al. 2023), video editing (Wang et al. 2023; Jiang et al. 2025) and video continuation (Y ang et al. 2024b).