Researchers from NVIDIA and Hugging Face have unveiled SANA-Video 2.0, a hybrid video diffusion transformer designed for efficient, high-resolution video generation. The model, available in 5B and 14B parameter scales, can produce 720p video on a single GPU while matching the quality of full-softmax video diffusion transformers (DiTs) at a fraction of the computational cost.
The key innovation is Hybrid Linear-Softmax Attention, which combines gated linear attention (O(N) complexity) with periodic gated-softmax anchors at a 3:1 ratio. This hybrid approach restores full-rank token interactions that pure linear attention lacks, avoiding quadratic complexity while maintaining expressiveness. Additionally, Block Attention Residuals (AttnRes) propagate completed block summaries into later linear layers, boosting deep-layer effective rank by ~12%.
In benchmarks, SANA-Video 2.0 achieves a VBench score of 84.30 at 480p in just 13.2 seconds on a single H100 GPU with 40-step sampling. Its compiled DiT forward pass is 3.2x faster than a matched full-softmax baseline at 720p/60s, and this gap widens with longer video durations. With full-stack Sol-Engine optimization (kernel fusion, caching, sparse attention), the 5B pipeline runs in 13.06s at 720p/5s, making it 120x faster than Wan 2.2-A14B on one H100.
The model is trained from scratch, learning the hybrid architecture directly rather than linearizing pretrained models. Proxy studies at reduced resolution established 25% softmax as the optimal quality-efficiency trade-off. The code is available on GitHub.