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🚀 Sana Model

Sana is a text - to - image framework. It can efficiently generate high - resolution (up to 4096 × 4096) and 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 https://github.com/NVlabs/Sana. For research purposes, we recommend using the generative - models Github repository (https://github.com/NVlabs/Sana) for training and inference, which integrates advanced diffusion samplers like Flow - DPM - Solver. MIT Han - Lab provides free Sana inference.

✨ Features

• High - resolution Image Generation: Can generate images up to 4096 × 4096 resolution.
• Fast Generation Speed: Capable of generating high - quality images with strong text - image alignment at a remarkably fast speed, and can be deployed on laptop GPUs.

📚 Documentation

Model

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 provides free Sana inference.

• Repository: https://github.com/NVlabs/Sana
• Demo: https://nv-sana.mit.edu/

🧨 Diffusers

PR developing: Sana and DC - AE

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.