Nvidia's New Long-Form Video Generation (12 minute read)

# SANA-Video 2.0 Source: [https://nvlabs.github.io/Sana/Video2/](https://nvlabs.github.io/Sana/Video2/) NVIDIA Research · Efficient AI Team & Singapore Lab Hybrid Linear Attention with Attention Residuals for Efficient Video Generation Junsong Chen,Jincheng Yu,Yitong Li,Shuchen Xue,Haozhe Liu,Jingyu Xin,Yuyang Zhao,Tian Ye,Zhangjie Wu,Zian Wang,Daquan Zhou,Ping Luo,Song Han,Enze Xie NVIDIA **84\.30**VBench Total**13\.06s**720p/5s · one H100**3\.2×**faster than softmax at 60 s**120×**faster than Wan 2\.2 14B ## One\-H100 latency 720p / 5s · one H100 · 40 steps **120×**Wan 2\.2 14B**1556** Hunyuan**788** LTX\-2\.3**130** Ours 14B**69\.3** Ours 5B \+ Sol**13\.06** log scaleseconds ↓ Paper overview ## Abstract Hybrid Linear Attention with Attention Residuals for Efficient Video Generation We introduce**SANA\-Video 2\.0**, a hybrid video diffusion transformer instantiated at 5B and 14B scales under a unified architecture\. Designed to generate high\-quality video up to 720p on a single GPU, SANA\-Video 2\.0 matches full\-softmax video DiTs in quality while retaining the favorable long\-sequence scaling of linear attention\. To avoid quadratic attention throughout,**Hybrid Linear\-Softmax Attention**combines gated linear attention for O\(N\)\-dominated mixing with periodic gated\-softmax anchors at a 3:1 ratio, restoring the full\-rank token interactions that pure linear attention lacks\. To propagate these refreshed representations across depth,**Block Attention Residuals \(AttnRes\)**route completed block summaries into later linear layers, enabling anchor\-feature reuse and boosting deep\-layer effective rank by ~12%\. Through from\-scratch training, SANA\-Video 2\.0 learns the complete hybrid directly rather than linearizing pretrained models, with reduced\-resolution proxy studies establishing 25% softmax as the optimal quality\-efficiency trade\-off\. With 40\-step sampling, SANA\-Video 2\.0 achieves a VBench score of 84\.30 in 13\.2s at 480p on a single H100, remaining competitive with far larger softmax video DiTs at a fraction of the latency\. Its compiled DiT forward pass is**3\.2× faster than a matched full\-softmax baseline**at 720p/60s, a gap that expands with video duration\. Furthermore, full\-stack Sol\-Engine optimization \(kernel fusion, caching, and sparse attention\) accelerates this hardware\-friendly backbone by a further**3\.58×**, bringing the 5B pipeline to**13\.06s at 720p/5s**and making it**120× faster than Wan 2\.2\-A14B**on one H100\. Overall, our hybrid design recovers softmax\-level expressiveness at substantially reduced cost, unlocking scalable long, high resolution video generation\. Paper ## SANA\-Video 2\.0 Hybrid Linear Attention with Attention Residuals for Efficient Video Generation BibTeX ``` @misc{chen2026sanavideo20hybridlinear, title = {SANA-Video 2.0: Hybrid Linear Attention with Attention Residuals for Efficient Video Generation}, author = {Junsong Chen and Jincheng Yu and Yitong Li and Shuchen Xue and Haozhe Liu and Jingyu Xin and Yuyang Zhao and Tian Ye and Zhangjie Wu and Zian Wang and Daquan Zhou and Ping Luo and Song Han and Enze Xie}, year = {2026}, eprint = {2607.21553}, archivePrefix = {arXiv}, primaryClass = {cs.CV}, doi = {10.48550/arXiv.2607.21553}, url = {https://arxiv.org/abs/2607.21553} } ```