README.md

---
library_name: sana
tags:
- text-to-image
- Sana
- 1024px_based_image_size
- Multi-language
language:
- en
- zh
base_model:
- Efficient-Large-Model/Sana_1600M_1024px_MultiLing
pipeline_tag: text-to-image
---

<p align="center" style="border-radius: 10px">
  <img src="https://raw.githubusercontent.com/NVlabs/Sana/refs/heads/main/asset/logo.png" width="35%" alt="logo"/>
</p>

<div style="display:flex;justify-content: center">
  <a href="https://huggingface.co/collections/Efficient-Large-Model/sana-673efba2a57ed99843f11f9e"><img src="https://img.shields.io/static/v1?label=Demo&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/"><img src="https://img.shields.io/static/v1?label=Project&message=Github&color=blue&logo=github-pages"></a> &ensp;
  <a href="https://hanlab.mit.edu/projects/sana/"><img src="https://img.shields.io/static/v1?label=Page&message=MIT&color=darkred&logo=github-pages"></a> &ensp;
  <a href="https://arxiv.org/abs/2410.10629"><img src="https://img.shields.io/static/v1?label=Arxiv&message=Sana&color=red&logo=arxiv"></a> &ensp;
  <a href="https://nv-sana.mit.edu/"><img src="https://img.shields.io/static/v1?label=Demo&message=MIT&color=yellow"></a> &ensp;
  <a href="https://discord.gg/rde6eaE5Ta"><img src="https://img.shields.io/static/v1?label=Discuss&message=Discord&color=purple&logo=discord"></a> &ensp;
</div>

# Model card

We introduce **Sana**, a text-to-image framework that can efficiently generate images up to 4096 × 4096 resolution.
Sana can synthesize high-resolution, high-quality images with strong text-image alignment at a remarkably fast speed, deployable on laptop GPU.

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


## Compare with base model

| Model                                                                                  | Language                   |
|----------------------------------------------------------------------------------------|----------------------------|
| [Sana_1600M_1024px](https://huggingface.co/Efficient-Large-Model/Sana_1600M_1024px)      | English                    |
| Sana_1600M_1024px_MultiLing                                                             | English, Chinese, Emoji    |


| Model                                                                             | Sample-1                                        | Sample-2                                                                          | Sample-3                                                     | Sample-4                                                                |
|-----------------------------------------------------------------------------------|-------------------------------------------------|-----------------------------------------------------------------------------------|--------------------------------------------------------------|-------------------------------------------------------------------------|
| [Sana_1600M_1024px](https://huggingface.co/Efficient-Large-Model/Sana_1600M_1024px) | <img src="assets/🐯 穿着 👕 吹 🎷0.jpg" width=256> | <img src="assets/猫 Wearing 🕶 flying on the 彩虹 with 🌹 in the ❄️0.jpg" width=256> | <img src="assets/🦁 teaching 🐯 to catch 🦋0.jpg" width=256> | <img src="assets/金色 🌅 下的长城, traditional Chinese style0.jpg" width=256> |
| Sana_1600M_1024px_MultiLing                                                        | <img src="assets/🐯 穿着 👕 吹 🎷1.jpg" width=256> | <img src="assets/猫 Wearing 🕶 flying on the 彩虹 with 🌹 in the ❄️1.jpg" width=256> | <img src="assets/🦁 teaching 🐯 to catch 🦋1.jpg" width=256> | <img src="assets/金色 🌅 下的长城, traditional Chinese style1.jpg" width=256> |
| Prompt                                                                            | 🐯 穿着 👕 吹 🎷                                   | 猫 Wearing 🕶 flying on the 彩虹 with 🌹 in the ❄️                                   | 🦁 teaching 🐯 to catch 🦋                                   | 金色 🌅 下的长城, traditional Chinese style                                   |

### Model Description

- **Developed by:** NVIDIA, Sana
- **Model type:** Linear-Diffusion-Transformer-based text-to-image generative model
- **Model size:** 1648M parameters
- **Model resolution:** This model is developed to generate 1024px based images with multi-scale heigh and width.
- **License:** [CC BY-NC-SA 4.0 License](./LICENSE.txt)
- **Model Description:** This is a model that can be used to generate and modify images based on text prompts. 
It is a Linear Diffusion Transformer that uses one fixed, pretrained text encoders ([Gemma2-2B-IT](https://huggingface.co/google/gemma-2-2b-it))
and one 32x spatial-compressed latent feature encoder ([DC-AE](https://hanlab.mit.edu/projects/dc-ae)).
- **Special:** This model is fine-tuned from the base model [Efficient-Large-Model/Sana_1600M_1024px](https://huggingface.co/Efficient-Large-Model/Sana_1600M_1024px) and it supports Emoji, Chinese and English and all mixed prompts.
- **Resources for more information:** Check out our [GitHub Repository](https://github.com/NVlabs/Sana) and the [Sana report on arXiv](https://arxiv.org/abs/2410.10629).

### 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 and for which most advanced diffusion sampler like Flow-DPM-Solver is integrated.
[MIT Han-Lab](https://nv-sana.mit.edu/) provides free Sana inference.
- **Repository:** ttps://github.com/NVlabs/Sana
- **Demo:** https://nv-sana.mit.edu/

### 🧨 Diffusers 

PR developing: [Sana](https://github.com/huggingface/diffusers/pull/9982) and [DC-AE](https://github.com/huggingface/diffusers/pull/9708)


## 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 image generation models are impressive, they can also reinforce or exacerbate social biases.