Timestep Embedding Tells: It's Time to Cache for Video Diffusion Model
Feng Liu, Shiwei Zhang, Xiaofeng Wang, Yujie Wei, Haonan Qiu, Yuzhong Zhao, Yingya Zhang, Qixiang Ye, Fang Wan
2025Year
91Top-tier citations
Abstract
refine the estimated differences and utilizes them to indicate output caching. Experiments show that TeaCache achieves up to 4.41× acceleration over Open-Sora-Plan with negligible (-0.07% Vbench score) degradation of visual quality.
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Cited by top-tier papers91
- Frame Context Packing and Drift Prevention in Next-Frame-Prediction Video Diffusion ModelsLvmin Zhang, Shengqu Cai, Muyang Li, Gordon Wetzstein et al.NeurIPS 2025 · 132 citations
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- FlashVSR: Towards Real-time Diffusion-Based Streaming Video Super ResolutionJunhao Zhuang, Shi Guo, Xin Cai, Xiaohui Li et al.CVPR 2026 · 42 citations
- VMoBA: Mixture-of-Block Attention for Video Diffusion ModelsJianzong Wu, Liang Hou, Haotian Yang, Ye Tian et al.ICLR 2026 · 36 citations
Builds on28
- Denoising Diffusion Probabilistic ModelsJonathan Ho, Ajay Jain, Pieter AbbeelNeurIPS 2020 · 35,902 citations
- High-Resolution Image Synthesis with Latent Diffusion ModelsRobin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser et al.CVPR 2022 · 13,123 citations
- Denoising Diffusion Implicit ModelsJiaming Song, Chenlin Meng, Stefano ErmonICLR 2021 · 11,743 citations
- Photorealistic Text-to-Image Diffusion Models with Deep Language UnderstandingChitwan Saharia, William Chan, Saurabh Saxena, Lala Li et al.NeurIPS 2022 · 8,965 citations
- Scalable Diffusion Models with TransformersWilliam Peebles, Saining XieICCV 2023 · 5,568 citations
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