---
license: mit
tags:
- vision
- generation
datasets:
- Laion2B-en
---

# Versatile Diffusion (v1.0, four-flow)

We built **Versatile Diffusion (VD), the first unified multi-flow multimodal diffusion framework**, as a step towards **Universal Generative AI**. Versatile Diffusion can natively support image-to-text, image-variation, text-to-image, and text-variation, and can be further extended to other applications such as semantic-style disentanglement, image-text dual-guided generation, latent image-to-text-to-image editing, and more. Future versions will support more modalities such as speech, music, video and 3D.

Resources for more information: [GitHub](https://github.com/SHI-Labs/Versatile-Diffusion), [arXiv](https://arxiv.org/abs/2211.08332).

# Model Description

One single flow of Versatile Diffusion contains a VAE, a diffuser, and a context encoder,  and thus handles one task (e.g., text-to-image) under one data type (e.g., image) and one context type (e.g., text). The multi-flow structure of Versatile Diffusion shows in the following diagram:

<p align="center">
  <img src="assets/figures/VD_framework.png" width="99%">
</p>

# Cautions, Biases, and Content Acknowledgment

We would like the raise the awareness of users of this demo of its potential issues and concerns. Like previous large foundation models, Versatile Diffusion could be problematic in some cases, partially due to the imperfect training data and pretrained network (VAEs / context encoders) with limited scope. In its future research phase, VD may do better on tasks such as text-to-image, image-to-text, etc., with the help of more powerful VAEs, more sophisticated network designs, and more cleaned data. So far, we have kept all features available for research testing both to show the great potential of the VD framework and to collect important feedback to improve the model in the future. We welcome researchers and users to report issues with the HuggingFace community discussion feature or email the authors.

Beware that VD may output content that reinforces or exacerbates societal biases, as well as realistic faces, pornography, and violence. VD was trained on the LAION-2B dataset, which scraped non-curated online images and text, and may contain unintended exceptions as we removed illegal content. VD in this demo is meant only for research purposes.

# Citation

```
@article{xu2022versatile,
	title        = {Versatile Diffusion: Text, Images and Variations All in One Diffusion Model},
	author       = {Xingqian Xu, Zhangyang Wang, Eric Zhang, Kai Wang, Humphrey Shi},
	year         = 2022,
	url          = {https://arxiv.org/abs/2211.08332},
	eprint       = {2211.08332},
	archiveprefix = {arXiv},
	primaryclass = {cs.CV}
}
```