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<div align='center'>
<h1>Emu: An Open Multimodal Generalist</h1h1>
<h3><a href="https://arxiv.org/abs/2307.05222">Generative Pretraining in Multimodality</a></h3>
[Quan Sun](https://github.com/Quan-Sun)<sup>1*</sup>, [Qiying Yu](https://yqy2001.github.io)<sup>2,1*</sup>, [Yufeng Cui]()<sup>1*</sup>, [Fan Zhang]()<sup>1*</sup>, [Xiaosong Zhang](https://github.com/zhangxiaosong18)<sup>1*</sup>, [Yueze Wang]()<sup>1</sup>, [Hongcheng Gao]()<sup>1</sup>, [Jingjing Liu](https://air.tsinghua.edu.cn/en/info/1046/1194.htm)<sup>2</sup>, [Tiejun Huang](https://scholar.google.com/citations?user=knvEK4AAAAAJ&hl=en)<sup>1,3</sup>, [Xinlong Wang](https://www.xloong.wang/)<sup>1</sup>
<sup>1</sup> [BAAI](https://www.baai.ac.cn/english.html), <sup>2</sup> [THU](https://air.tsinghua.edu.cn), <sup>3</sup> [PKU](https://english.pku.edu.cn/) <br><sup>*</sup> Equal Contribution
| [Paper](https://arxiv.org/abs/2307.05222) | [Demo(tmp)](http://218.91.113.230:9002/) |
</div>
**Emu** is a Large Multimodal Model (LMM) trained with a unified autoregressive objective, *i.e.*, predict-the-next-element, including both visual embeddings and textual tokens. Trained under this objective, **Emu** can serve as a generalist interface for diverse multimodal tasks, such as image captioning, image/video question answering, and text-to-image generation, together with new abilities like in-context text and image generation, and image blending.
## Setup
Clone the github repository and install required packages:
```shell
git clone https://github.com/baaivision/Emu
cd Emu
pip install -r requirements.txt
```
## Model Weights
We release the pretrained and instruction-tuned weights of **Emu**. Our weights are subject to LLaMA's [license](https://github.com/facebookresearch/llama/blob/main/LICENSE).
| Model name | Weight |
| ---------- | ------------------------------------------------------- |
| **Emu** | [🤗 HF link](https://huggingface.co/BAAI/Emu/blob/main/Emu-pretrain.pt) (27GB) |
| **Emu-I** | [🤗 HF link](https://huggingface.co/BAAI/Emu/blob/main/Emu-instruct.pt) (27GB) |
## Model Usage
At present, we provide inference code for image captioning and visual question answering:
```sh
python emu_inference.py --instruct --ckpt-path $Instruct_CKPT_PATH
```
## Citation
If you find Emu useful for your your research and applications, please consider citing:
```
@article{Emu,
title={Generative Pretraining in Multimodality},
author={Sun, Quan and Yu, Qiying and Cui, Yufeng and Zhang, Fan and Zhang, Xiaosong and Wang, Yueze and Gao, Hongcheng and Liu, Jingjing and Huang, Tiejun and Wang, Xinlong},
publisher={arXiv:2307.05222},
year={2023},
}