LaVIT: Empower the Large Language Model to Understand and Generate Visual Content

This is the official repository for the multi-modal large language models: LaVIT and Video-LaVIT. The LaVIT project aims to leverage the exceptional capability of LLM to deal with visual content. The proposed pre-training strategy supports visual understanding and generation with one unified framework.

• Unified Language-Vision Pretraining in LLM with Dynamic Discrete Visual Tokenization, ICLR 2024, [arXiv] [BibTeX]

• Video-LaVIT: Unified Video-Language Pre-training with Decoupled Visual-Motional Tokenization, [arXiv] [Project] [BibTeX]

News and Updates

• 2024.04.21 🚀🚀🚀 We have released the pre-trained weight for Video-LaVIT on the HuggingFace and provide the inference code.

• 2024.02.05 🌟🌟🌟 We have proposed the Video-LaVIT: an effective multimodal pre-training approach that empowers LLMs to comprehend and generate video content in a unified framework.

• 2024.01.15 👏👏👏 LaVIT has been accepted by ICLR 2024!

• 2023.10.17 🚀🚀🚀 We release the pre-trained weight for LaVIT on the HuggingFace and provide the inference code of using it for both multi-modal understanding and generation.

Introduction

The LaVIT and Video-LaVIT are general-purpose multi-modal foundation models that inherit the successful learning paradigm of LLM: predicting the next visual/textual token in an auto-regressive manner. The core design of the LaVIT series works includes a visual tokenizer and a detokenizer. The visual tokenizer aims to translate the non-linguistic visual content (e.g., image, video) into a sequence of discrete tokens like a foreign language that LLM can read. The detokenizer recovers the generated discrete tokens from LLM to the continuous visual signals.

After pre-training, LaVIT and Video-LaVIT can support * Read image and video content, generate the captions, and answer the questions. * Text-to-image, Text-to-Video and Image-to-Video generation. * Generation via Multi-modal Prompt. ## Citation Consider giving this repository a star and cite LaVIT in your publications if it helps your research. ``` @article{jin2023unified, title={Unified Language-Vision Pretraining in LLM with Dynamic Discrete Visual Tokenization}, author={Jin, Yang and Xu, Kun and Xu, Kun and Chen, Liwei and Liao, Chao and Tan, Jianchao and Mu, Yadong and others}, journal={arXiv preprint arXiv:2309.04669}, year={2023} } @article{jin2024video, title={Video-LaVIT: Unified Video-Language Pre-training with Decoupled Visual-Motional Tokenization}, author={Jin, Yang and Sun, Zhicheng and Xu, Kun and Chen, Liwei and Jiang, Hao and Huang, Quzhe and Song, Chengru and Liu, Yuliang and Zhang, Di and Song, Yang and others}, journal={arXiv preprint arXiv:2402.03161}, year={2024} }