README.md

---
library_name: sana
tags:
- text-to-image
- SANA-1.5
- 1024px_based_image_size
- BF16
language:
- en
- zh
base_model:
- Efficient-Large-Model/SANA1.5_4.8B_1024px
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-15-67d6803867cb21c230b780e4"><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/Sana-1.5/"><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/2501.18427"><img src="https://img.shields.io/static/v1?label=Arxiv&message=SANA-1.5&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>

# 🐱 Sana Model Card

## Model

<p align="center" border-raduis="10px">
  <img src="https://nvlabs.github.io/Sana/Sana-1.5/asset/content/pipeline.png" width="80%" alt="teaser_page1"/>
</p>

We introduce **SANA-1.5**，an efficient model with scaling of training-time and inference time techniques. 
SANA-1.5 delivers: **efficient model growth** from 1.6B Sana-1.0 model to 4.8B, achieving similar or better performance than training from scratch and saving 60% training cost; 
**efficient model depth pruning**, slimming any model size as you want; 
powerful VLM selection based **inference scaling**, smaller model+inference scaling > larger model; 
Top-notch GenEval & DPGBench results. Detailed results are shown in the below table.

<p align="center" border-raduis="10px">
  <img src="https://nvlabs.github.io/Sana/Sana-1.5/asset/content/geneval_comparison.png" alt="model growth performance on GenEval" class="inserted-image"
       style="max-width: 45%; height: auto; display: inline-block;">
  <img src="https://nvlabs.github.io/Sana/Sana-1.5/asset/content/optimizer_loss_comparison_with_ema.png" alt="8-bit optimizer" class="inserted-image"
       style="max-width: 45%; height: auto; display: inline-block;">
</p>

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

### Model Description

- **Developed by:** NVIDIA, Sana
- **Model type:** Scalable Linear-Diffusion-Transformer-based text-to-image generative model
- **Model size:** 4.8B parameters
- **Model precision:** torch.bfloat16 (BF16)
- **Model resolution:** This model is developed to generate 1024px based images with multi-scale heigh and width.
- **License:** [NSCL v2-custom](./LICENSE.txt). Governing Terms:  NVIDIA License.  Additional Information:  [Gemma Terms of Use  |  Google AI for Developers](https://ai.google.dev/gemma/terms) for Gemma-2-2B-IT, [Gemma Prohibited Use Policy  |  Google AI for Developers](https://ai.google.dev/gemma/prohibited_use_policy).
- **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)).
- **Resources for more information:** Check out our [GitHub Repository](https://github.com/NVlabs/Sana) and the [SANA-1.5 report on arXiv](https://arxiv.org/abs/2501.18427).

### 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 

Developing


## 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.