SANA-Video: Efficient Video Generation with Block Linear Diffusion
  Transformer
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.
68db4b9ad2bf1f4b15ec7425
Junsong Chen
claimed_verified
2025-09-30T06:29:43.494Z
645b5b09bc7518912e1f9733
/avatars/4d35f728b41f93881a9b67c337f4d1df.svg
Chen
user
Lawrence-cj
68db4b9ad2bf1f4b15ec7426
Yuyang Zhao
68db4b9ad2bf1f4b15ec7427
Jincheng Yu
68db4b9ad2bf1f4b15ec7428
Ruihang Chu
68db4b9ad2bf1f4b15ec7429
Junyu Chen
68db4b9ad2bf1f4b15ec742a
Shuai Yang
68db4b9ad2bf1f4b15ec742b
Xianbang Wang
68db4b9ad2bf1f4b15ec742c
Yicheng Pan
68db4b9ad2bf1f4b15ec742d
Daquan Zhou
68db4b9ad2bf1f4b15ec742e
Huan Ling
68db4b9ad2bf1f4b15ec742f
Haozhe Liu
68db4b9ad2bf1f4b15ec7430
Hongwei Yi
68db4b9ad2bf1f4b15ec7431
Hao Zhang
68db4b9ad2bf1f4b15ec7432
Muyang Li
claimed_verified
2025-09-30T06:29:40.674Z
63129589bbaa385279d1826e
https://cdn-avatars.huggingface.co/v1/production/uploads/63129589bbaa385279d1826e/0AUs3q4ngRZ-wXuY1jP9G.jpeg
Muyang Li
user
Lmxyy
68db4b9ad2bf1f4b15ec7433
Yukang Chen
claimed_verified
2025-10-13T10:10:46.009Z
62919485a29097b211bc7b83
https://cdn-avatars.huggingface.co/v1/production/uploads/62919485a29097b211bc7b83/TX8iBGu5JSuFlrRvjPEBV.png
YukangChen
user
Yukang
68db4b9ad2bf1f4b15ec7434
Han Cai
68db4b9ad2bf1f4b15ec7435
Sanja Fidler
68db4b9ad2bf1f4b15ec7436
Ping Luo
68db4b9ad2bf1f4b15ec7437
Song Han
68db4b9ad2bf1f4b15ec7438
Enze Xie
SANA-Video: Efficient Video Generation with Block Linear Diffusion
  Transformer
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.
SANA-Video, a small diffusion model, efficiently generates high-resolution, high-quality videos with strong text-video alignment using linear attention and a constant-memory KV cache, achieving competitive performance at a lower cost and faster speed.
diffusion model Linear DiT linear attention constant-memory KV cache block-wise autoregressive text-video alignment MovieGen NVFP4 precision