README.md

---
license: other
license_name: nvidia-open-model-license
license_link: >-
  https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/
library_name: sana, sana-video
tags:
- text-to-video
- SANA-Video
- 720p_5s_pretrained_model
- BF16
- diffusion
- LTX2-VAE
language:
- en
- zh
base_model:
- Efficient-Large-Model/SANA-Video_2B_720p
pipeline_tag: text-to-video
---
<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

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  <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;">
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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](https://github.com/Lightricks/LTX-2)).
- **Resources for more information:** Check out our [GitHub Repository](https://github.com/NVlabs/Sana) and the [SANA-Video report on arXiv](https://arxiv.org/pdf/2509.24695).

### 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

GOVERNING TERMS: This trial service is governed by the [NVIDIA API Trial Terms of Service](https://assets.ngc.nvidia.com/products/api-catalog/legal/NVIDIA%20API%20Trial%20Terms%20of%20Service.pdf). Use of this model is governed by the [NVIDIA Open Model License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/).

## 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.