SANA-Video_2B_720p

<p align="center" style="border-radius: 10px"> <img src="https://cdn-uploads.huggingface.co/production/uploads/645b5b09bc7518912e1f9733/N0VlE-y1pau-4O1RlijQd.png" width="98%" alt="logo"/> </p> <div style="display:flex;justify-content: center"> <a href="https://hf.co/collections/Efficient-Large-Model/sana-video"><img src="https://img.shields.io/static/v1?label=Weights&message=Huggingface&color=yellow"></a> &ensp; <a href="https://github.com/NVlabs/Sana"><img src="https://img.shields.io/static/v1?label=Code&message=Github&color=blue&logo=github"></a> &ensp; <a href="https://nvlabs.github.io/Sana/Video/"><img src="https://img.shields.io/static/v1?label=Project&message=Github&color=blue&logo=github-pages"></a> &ensp; <a href="https://arxiv.org/pdf/2509.24695"><img src="https://img.shields.io/static/v1?label=Arxiv&message=SANA-Video&color=red&logo=arxiv"></a> &ensp; </div>

🐱 SANA-Video Model Card

<!-- <div align="center"> <a href="https://www.youtube.com/watch?v=nI_Ohgf8eOU" target="_blank"> <img src="https://img.youtube.com/vi/nI_Ohgf8eOU/0.jpg" alt="Demo Video of SANA-Video" style="width: 48%; display: block; margin: 0 auto; display: inline-block;"> </a> <a href="https://www.youtube.com/watch?v=OOZzkirgsAc" target="_blank"> <img src="https://img.youtube.com/vi/OOZzkirgsAc/0.jpg" alt="Demo Video of SANA-Video" style="width: 48%; display: block; margin: 0 auto; display: inline-block;"> </a> </div> -->

SANA-Video is a small, ultra-efficient diffusion model designed for rapid generation of high-quality, minute-long videos at resolutions up to 720×1280.

Key innovations and efficiency drivers include:

(1) Linear DiT: Leverages linear attention as the core operation, offering significantly more efficiency than vanilla attention when processing the massive number of tokens required for video generation.

(2) Constant-Memory KV Cache for Block Linear Attention: Implements a block-wise autoregressive approach that uses the cumulative properties of linear attention to maintain global context at a fixed memory cost, eliminating the traditional KV cache bottleneck and enabling efficient, minute-long video synthesis.

SANA-Video achieves exceptional efficiency and cost savings: its training cost is only 1% of MovieGen's (12 days on 64 H100 GPUs). Compared to modern state-of-the-art small diffusion models (e.g., Wan 2.1 and SkyReel-V2), SANA-Video maintains competitive performance while being 16× faster in measured latency. SANA-Video is deployable on RTX 5090 GPUs, accelerating the inference speed for a 5-second 720p video from 71s down to 29s (2.4× speedup), setting a new standard for low-cost, high-quality video generation.

Source code is available at https://github.com/NVlabs/Sana.

🐱 How to Inference

Refer to: https://github.com/NVlabs/Sana/blob/main/asset/docs/sana_video.md#1-inference-with-txt-file

diffusers pipeline

refer to: https://huggingface.co/Efficient-Large-Model/SANA-Video_2B_720p_diffusers

Model Description

• Developed by: NVIDIA, Sana
• Model type: Efficient Video Generation with Block Linear Diffusion Transformer
• Model size: 2B parameters
• Model precision: torch.bfloat16 (BF16)
• Model resolution: This model is developed to generate 720p resolution 81(5s) frames videos with multi-scale heigh and width.
• Model Description: This is a model that can be used to generate and modify videos based on text prompts. It is a Linear Diffusion Transformer that uses LTX2-vae one 32x32x8 spatial-temporal-compressed latent feature encoder (LTX2).
• Resources for more information: Check out our GitHub Repository and the SANA-Video report on arXiv.

Model Sources

For research purposes, we recommend our generative-models Github repository (https://github.com/NVlabs/Sana), which is more suitable for both training and inference

• Repository: https://github.com/NVlabs/Sana
• Guidance: https://github.com/NVlabs/Sana/asset/docs/sana_video.md

License/Terms of Use

This model is released under the Apache License 2.0.

Uses

Direct Use

The model is intended for research purposes only. Possible research areas and tasks include

• Generation of artworks and use in design and other artistic processes.

• Applications in educational or creative tools.

• Research on generative models.

• Safe deployment of models which have the potential to generate harmful content.

• Probing and understanding the limitations and biases of generative models.

Excluded uses are described below.

Out-of-Scope Use

The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.

Limitations and Bias

Limitations

• The model does not achieve perfect photorealism
• The model cannot render complex legible text
• fingers, .etc in general may not be generated properly.
• The autoencoding part of the model is lossy.

Bias

While the capabilities of video generation models are impressive, they can also reinforce or exacerbate social biases.