SANA1.5 4.8B 1024px

🚀 🐱 Sana Model Card

This is a scalable Linear - Diffusion - Transformer - based text - to - image generative model, which can generate and modify images based on text prompts. It uses efficient techniques to achieve better performance while saving training costs.

           

✨ Features

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.

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

📚 Documentation

Model Description

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

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.

🔧 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.