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Efficient-Large-Model / Sana_1600M_512px_MultiLing
Model's Last Updated: July 31 2026
text-to-image
Introduction of Sana_1600M_512px_MultiLing
Model Details of Sana_1600M_512px_MultiLing
Model card
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 .
Compare with base model
| Model | Language |
|---|---|
| Sana_1600M_512px | English |
| Sana_1600M_512px_MultiLing | English, Chinese, Emoji |
| Model | Sample-1 | Sample-2 | Sample-3 | Sample-4 |
|---|---|---|---|---|
| Sana_1600M_512px |
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| Sana_1600M_512px_MultiLing |
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| Prompt | 🐯 穿着 👕 吹 🎷 | 猫 Wearing 🕶 flying on the 彩虹 with 🌹 in the ❄️ | 🦁 teaching 🐯 to catch 🦋 | 金色 🌅 下的长城, traditional Chinese style |
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 512px 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 ).
- Special: This model is fine-tuned from the base model Efficient-Large-Model/Sana_1600M_512px and it supports Emoji, Chinese and English and all mixed prompts.
- 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.
- Repository: ttps://github.com/NVlabs/Sana
- Demo: https://nv-sana.mit.edu/
🧨 Diffusers
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.
Runs of Efficient-Large-Model Sana_1600M_512px_MultiLing on huggingface.co
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More Information About Sana_1600M_512px_MultiLing huggingface.co Model
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Updated:December 13 2024
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