Emu is a multimodal generalist that can seamlessly generate images and texts in multimodal context. Emu is 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 both image-to-text and text-to-image tasks.

Emu serves as a generalist interface capable of 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:

Clone this repository and install required packages:

git clone https://github.com/baaivision/Emu
cd Emu

pip install -r requirements.txt

We release the pretrained and instruction-tuned weights of Emu. Our weights are subject to LLaMA's license.

At present, we provide inference code that can process interleaved image-text as input, and output text.

For instruction-tuned model, we provide examples for image captioning, visual question answering, and interleaved multi-image understanding:

python inference.py --instruct --ckpt-path $Instruct_CKPT_PATH

For pretrained model, we provide an example for in-context learning:

python inference.py --ckpt-path $Pretrain_CKPT_PATH

We are committed to open-sourcing all Emu related materials, including:

• The weights of Emu and Emu-I
• Inference example for interleaved image-text as input, text as output
• Video inference example
• Weights of image decoder & image generation/blending example
• YT-Storyboard-1B pretraining data
• Pretraining code
• Instruction tuning code
• Evaluation code

We hope to foster the growth of our community through open-sourcing and promoting collaboration👬. Let's step towards multimodal intelligence together🍻.

We thank the great work from LLaMA, BLIP-2, Stable Diffusion, and FastChat.

If you find Emu useful for your research and applications, please consider starring this repository and 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 preprint arXiv:2307.05222},
  year={2023},
}