SANA-Video: Efficient Video Generation with Block Linear Diffusion
  Transformer
Authors: name: Junsong Chen, name: Yuyang Zhao, name: Jincheng Yu, name: Ruihang Chu, name: Junyu Chen, name: Shuai Yang, name: Xianbang Wang, name: Yicheng Pan, name: Daquan Zhou, name: Huan Ling, name: Haozhe Liu, name: Hongwei Yi, name: Hao Zhang, name: Muyang Li, name: Yukang Chen, name: Han Cai, name: Sanja Fidler, name: Ping Luo, name: Song Han, name: Enze Xie
Organization: NVIDIA

We introduce SANA-Video, a small diffusion model that can efficiently
generate videos up to 720x1280 resolution and minute-length duration.
SANA-Video synthesizes high-resolution, high-quality and long videos with
strong text-video alignment at a remarkably fast speed, deployable on RTX 5090
GPU. Two core designs ensure our efficient, effective and long video
generation: (1) Linear DiT: We leverage linear attention as the core operation,
which is more efficient than vanilla attention given the large number of tokens
processed in video generation. (2) Constant-Memory KV cache for Block Linear
Attention: we design block-wise autoregressive approach for long video
generation by employing a constant-memory state, derived from the cumulative
properties of linear attention. This KV cache provides the Linear DiT with
global context at a fixed memory cost, eliminating the need for a traditional
KV cache and enabling efficient, minute-long video generation. In addition, we
explore effective data filters and model training strategies, narrowing the
training cost to 12 days on 64 H100 GPUs, which is only 1% of the cost of
MovieGen. Given its low cost, SANA-Video achieves competitive performance
compared to modern state-of-the-art small diffusion models (e.g., Wan 2.1-1.3B
and SkyReel-V2-1.3B) while being 16x faster in measured latency. Moreover,
SANA-Video can be deployed on RTX 5090 GPUs with NVFP4 precision, accelerating
the inference speed of generating a 5-second 720p video from 71s to 29s (2.4x
speedup). In summary, SANA-Video enables low-cost, high-quality video
generation.