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🚀 Sana, Sana-Sprint

Sana and Sana-Sprint are ultra - efficient text - to - image diffusion models, reducing inference steps while achieving state - of - the - art performance.

           

🚀 Quick Start

The source code of Sana and Sana - Sprint is available at https://github.com/NVlabs/Sana. You can refer to it for further development and research.

✨ Features

Demos

Training Pipeline

Model Efficiency

SANA - Sprint is an ultra - efficient diffusion model for text - to - image (T2I) generation, reducing inference steps from 20 to 1 - 4 while achieving state - of - the - art performance. Key innovations include: (1) A training - free approach for continuous - time consistency distillation (sCM), eliminating costly retraining; (2) A unified step - adaptive model for high - quality generation in 1 - 4 steps; and (3) ControlNet integration for real - time interactive image generation. SANA - Sprint achieves 7.59 FID and 0.74 GenEval in just 1 step — outperforming FLUX - schnell (7.94 FID / 0.71 GenEval) while being 10× faster (0.1s vs 1.1s on H100). With latencies of 0.1s (T2I) and 0.25s (ControlNet) for 1024×1024 images on H100, and 0.31s (T2I) on an RTX 4090, SANA - Sprint is ideal for AI - powered consumer applications (AIPC).

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. [MIT Han - Lab](https://nv - sana.mit.edu/sprint) provides free SANA - Sprint inference.

• Repository: https://github.com/NVlabs/Sana
• Demo: https://nv - sana.mit.edu/sprint
• Guidance: https://github.com/NVlabs/Sana/asset/docs/sana_sprint.md

📚 Documentation

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.

📄 License

The model is licensed 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.