Efficient-Large-Model/Sana_1600M_1024px_MultiLing
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Model card
We introduce Sana, a text-to-image framework that can efficiently generate images up to 4096 Γ 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 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: Apache License 2.0. Additional Information: Gemma Terms of Use | Google AI for Developers for Gemma-2-2B-IT, Gemma Prohibited Use Policy | Google AI for Developers.
- 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_1024px 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/
𧨠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.