# SANA-Video 2.0

> Source: <https://nvlabs.github.io/Sana/Video2/>
> Published: 2026-07-24 06:34:56+00:00

NVIDIA Research · Efficient AI Team & Singapore Lab

# SANA-Video 2.0

Hybrid Linear Attention with Attention Residuals for Efficient Video Generation

**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×**

**1556**

**788**

**130**

**69.3**

**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.