Sana 1600M 1024px MultiLing

🚀 Sana

Sana is a text - to - image framework that can efficiently generate high - resolution images up to 4096 × 4096. It can synthesize high - quality images with strong text - image alignment at a fast speed and can be deployed on laptop GPUs.

             

🚀 Quick Start

The source code of Sana is available at GitHub. You can explore the model through the provided links above.

✨ Features

• High - Resolution Image Generation: Capable of generating images up to 4096 × 4096 resolution.
• Fast and High - Quality: Can synthesize high - quality images with strong text - image alignment at a remarkable speed, and is deployable on laptop GPUs.
• Multi - language Support: Supports English, Chinese, and Emoji, as well as all mixed prompts.

📚 Documentation

Compare with base model

Model	Sample - 1	Sample - 2	Sample - 3	Sample - 4
Sana_1600M_1024px
Sana_1600M_1024px_MultiLing
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 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: https://github.com/NVlabs/Sana
• Demo: https://nv - sana.mit.edu/

🧨 Diffusers

PR developing: Sana and DC - AE

💡 Usage

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.

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.

🔧 Technical Details

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.

📄 License

The model is under NSCL v2 - custom. Governing Terms: NVIDIA License. Additional Information: Gemma Terms of Use | Google AI for Developers for Gemma - 2 - 2B - IT, Gemma Prohibited Use Policy | Google AI for Developers.