Sana_1600M_1024px Open-source Text-to-Image Framework - Free Deployment for Efficient Generation of 4096×4096 Ultra-high Resolution Images

Sana 1600M 1024px

Developed by Efficient-Large-Model

Sana is an efficient text-to-image framework capable of generating images up to 4096×4096 resolution, deployable on laptop GPUs.

Image Generation Supports Multiple Languages#High-resolution image generation #Linear Diffusion Transformer #Fast inference

Downloads 2,327

Release Time : 11/4/2024

Model Overview

Sana is a text-to-image generation model based on Linear Diffusion Transformer, capable of synthesizing high-resolution, high-quality images with strong text-image alignment at astonishing speeds.

Model Features

High-resolution image generation

Capable of generating images up to 4096×4096 resolution.

Efficient inference

Deployable on laptop GPUs with fast inference speed.

Strong text-image alignment

Generated images are highly aligned with input text prompts.

Multi-scale aspect ratio support

Supports multi-scale aspect ratio image generation based on 1024px.

Model Capabilities

Text-to-image generation

High-resolution image generation

Multi-scale image generation

Use Cases

Art and Design

Artwork generation

Used for generating artworks and design creative processes.

High-quality artworks

Educational tools

Used for developing educational or creative tools.

Enhanced learning experience

Research

Generative model research

Used for research and development of generative models.

Advancing generative model technology

Safe deployment research

Research on safe deployment of models with potential harmful content generation risks.

Improved model safety

🚀 Sana

Sana is a text - to - image framework that can efficiently generate high - resolution images up to 4096 × 4096. It synthesizes 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 Repository. You can explore the project through the following links:

✨ Features

High - Resolution Image Generation: Can generate images up to 4096 × 4096 resolution.
Fast and High - Quality: Synthesizes high - quality images with strong text - image alignment at a fast speed.
Laptop GPU Deployment: Can be deployed on laptop GPUs.

📚 Documentation

Model

teaser_page1

Model Description

Property	Details
Developed by	NVIDIA, Sana
Model Type	Linear - Diffusion - Transformer - based text - to - image generative model
Model Size	1648M parameters
Model Resolution	This model is developed to generate 1024px based images with multi - scale height and width.
License	NSCL v2 - custom. Governing Terms: NVIDIA License. Additional Information: [Gemma Terms of Use
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 and the Sana report on arXiv.

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

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.

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.

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