MiniGPT-V

MiniGPT-v2: Large Language Model as a Unified Interface for Vision-Language Multi-task Learning

Jun Chen, Deyao Zhu, Xiaoqian Shen, Xiang Li, Zechun Liu, Pengchuan Zhang, Raghuraman Krishnamoorthi, Vikas Chandra, Yunyang Xiong☨, Mohamed Elhoseiny☨

☨equal last author

MiniGPT-4: Enhancing Vision-language Understanding with Advanced Large Language Models

Deyao Zhu*, Jun Chen*, Xiaoqian Shen, Xiang Li, Mohamed Elhoseiny

*equal contribution

King Abdullah University of Science and Technology

💡 Get help - Q&A or Discord 💬

News

[Oct.13 2023] Breaking! We release the first major update with our MiniGPT-v2

[Aug.28 2023] We now provide a llama 2 version of MiniGPT-4

Online Demo

Click the image to chat with MiniGPT-v2 around your images

Click the image to chat with MiniGPT-4 around your images

MiniGPT-v2 Examples

MiniGPT-4 Examples

More examples can be found in the project page.

Getting Started

Installation

1. Prepare the code and the environment

Git clone our repository, creating a python environment and activate it via the following command

git clone https://github.com/Vision-CAIR/MiniGPT-4.git
cd MiniGPT-4
conda env create -f environment.yml
conda activate minigpt4

2. Prepare the pretrained LLM weights

MiniGPT-v2 is based on Llama2 Chat 7B. For MiniGPT-4, we have both Vicuna V0 and Llama 2 version. Download the corresponding LLM weights from the following huggingface space via clone the repository using git-lfs.

Then, set the variable llama_model in the model config file to the LLM weight path.

• For MiniGPT-v2, set the LLM path here at Line 14.

• For MiniGPT-4 (Llama2), set the LLM path here at Line 15.

• For MiniGPT-4 (Vicuna), set the LLM path here at Line 18

3. Prepare the pretrained model checkpoints

Download the pretrained model checkpoints

For MiniGPT-v2, set the path to the pretrained checkpoint in the evaluation config file in eval_configs/minigptv2_eval.yaml at Line 8.

For MiniGPT-4, set the path to the pretrained checkpoint in the evaluation config file in eval_configs/minigpt4_eval.yaml at Line 8 for Vicuna version or eval_configs/minigpt4_llama2_eval.yaml for LLama2 version.

Launching Demo Locally

For MiniGPT-v2, run

python demo_v2.py --cfg-path eval_configs/minigpt4v2_eval.yaml  --gpu-id 0

For MiniGPT-4 (Vicuna version), run

python demo.py --cfg-path eval_configs/minigpt4_eval.yaml  --gpu-id 0

For MiniGPT-4 (Llama2 version), run

python demo.py --cfg-path eval_configs/minigpt4_llama2_eval.yaml  --gpu-id 0

To save GPU memory, LLMs loads as 8 bit by default, with a beam search width of 1. This configuration requires about 23G GPU memory for 13B LLM and 11.5G GPU memory for 7B LLM. For more powerful GPUs, you can run the model in 16 bit by setting low_resource to False in the relevant config file:

• MiniGPT-v2: minigptv2_eval.yaml
• MiniGPT-4 (Llama2): minigpt4_llama2_eval.yaml
• MiniGPT-4 (Vicuna): minigpt4_eval.yaml

Thanks @WangRongsheng, you can also run MiniGPT-4 on Colab

Training

For training details of MiniGPT-4, check here.

Acknowledgement

• BLIP2 The model architecture of MiniGPT-4 follows BLIP-2. Don't forget to check this great open-source work if you don't know it before!
• Lavis This repository is built upon Lavis!
• Vicuna The fantastic language ability of Vicuna with only 13B parameters is just amazing. And it is open-source!
• LLaMA The strong open-sourced LLaMA 2 language model.

If you're using MiniGPT-4/MiniGPT-v2 in your research or applications, please cite using this BibTeX:

@article{chen2023minigptv2,
      title={MiniGPT-v2: large language model as a unified interface for vision-language multi-task learning}, 
      author={Chen, Jun and Zhu, Deyao and Shen, Xiaoqian and Li, Xiang and Liu, Zechu and Zhang, Pengchuan and Krishnamoorthi, Raghuraman and Chandra, Vikas and Xiong, Yunyang and Elhoseiny, Mohamed},
      year={2023},
      journal={arXiv preprint arXiv:2310.09478},
}

@article{zhu2023minigpt,
  title={MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models},
  author={Zhu, Deyao and Chen, Jun and Shen, Xiaoqian and Li, Xiang and Elhoseiny, Mohamed},
  journal={arXiv preprint arXiv:2304.10592},
  year={2023}
}

License

This repository is under BSD 3-Clause License. Many codes are based on Lavis with BSD 3-Clause License here.