Hugging Face's logo

Efficient-Large-Model
/
Sana_600M_1024px

Text-to-Image
Sana
English
Chinese
Sana
1024px_based_image_size
Model card Files Community
Sana_600M_1024px / README.md
Lawrence-cj's picture
Lawrence-cj
Create README.md
5319959 verified
preview code
|
raw
history blame contribute delete
4.73 kB
metadata
library_name: sana
tags:
  - text-to-image
  - Sana
  - 1024px_based_image_size
language:
  - en
  - zh
base_model:
  - Efficient-Large-Model/Sana_600M_1024px
pipeline_tag: text-to-image

logo

             

🐱 Sana Model Card

teaser_page1

Model

teaser_page1

We introduce Sana, a text-to-image framework that can efficiently generate images up to 4096 × 4096 resolution. Sana can synthesize high-resolution, high-quality images with strong text-image alignment at a remarkably fast speed, deployable on laptop GPU.

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

Model Description

  • 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 heigh and width.
  • License: CC BY-NC-SA 4.0 License
  • 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) and one 32x spatial-compressed latent feature encoder (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 provides free Sana inference.

🧨 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.