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

Deyao Zhu* (On Job Market!), Jun Chen* (On Job Market!), Xiaoqian Shen, Xiang Li, and Mohamed Elhoseiny. *Equal Contribution

King Abdullah University of Science and Technology

Online Demo

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

Examples

More examples can be found in the project page.

Introduction

• MiniGPT-4 aligns a frozen visual encoder from BLIP-2 with a frozen LLM, Vicuna, using just one projection layer.
• We train MiniGPT-4 with two stages. The first traditional pretraining stage is trained using roughly 5 million aligned image-text pairs in 10 hours using 4 A100s. After the first stage, Vicuna is able to understand the image. But the generation ability of Vicuna is heavilly impacted.
• To address this issue and improve usability, we propose a novel way to create high-quality image-text pairs by the model itself and ChatGPT together. Based on this, we then create a small (3500 pairs in total) yet high-quality dataset.
• The second finetuning stage is trained on this dataset in a conversation template to significantly improve its generation reliability and overall usability. To our surprise, this stage is computationally efficient and takes only around 7 minutes with a single A100.
• MiniGPT-4 yields many emerging vision-language capabilities similar to those demonstrated in GPT-4.

Getting Started

Installation

1. Prepare the code and the environment

Git clone our repository, creating a python environment and ativate 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 Vicuna weights

The current version of MiniGPT-4 is built on the v0 versoin of Vicuna-13B. Please refer to our instruction here to prepare the Vicuna weights. The final weights would be in a single folder with the following structure:

vicuna_weights
├── config.json
├── generation_config.json
├── pytorch_model.bin.index.json
├── pytorch_model-00001-of-00003.bin
...   

Then, set the path to the vicuna weight in the model config file here at Line 16.

3. Prepare the pretrained MiniGPT-4 checkpoint

To play with our pretrained model, download the pretrained checkpoint here. Then, set the path to the pretrained checkpoint in the evaluation config file in eval_configs/minigpt4_eval.yaml at Line 11.

Launching Demo Locally

Try out our demo demo.py on your local machine by running

python demo.py --cfg-path eval_configs/minigpt4_eval.yaml

Here, we load Vicuna as 8 bit by default to save some GPU memory usage. Besides, the default beam search width is 1. Under this setting, the demo cost about 23G GPU memory. If you have a more powerful GPU with larger GPU memory, you can run the model in 16 bit by setting low_resource to False in the config file minigpt4_eval.yaml and use a larger beam search width.

Training

The training of MiniGPT-4 contains two alignment stages.

1. First pretraining stage

In the first pretrained stage, the model is trained using image-text pairs from Laion and CC datasets to align the vision and language model. To download and prepare the datasets, please check our first stage dataset preparation instruction. After the first stage, the visual features are mapped and can be understood by the language model. To launch the first stage training, run the following command. In our experiments, we use 4 A100. You can change the save path in the config file train_configs/minigpt4_stage1_pretrain.yaml

torchrun --nproc-per-node NUM_GPU train.py --cfg-path train_configs/minigpt4_stage1_pretrain.yaml

2. Second finetuning stage

In the second stage, we use a small high quality image-text pair dataset created by ourselves and convert it to a conversation format to further align MiniGPT-4. To download and prepare our second stage dataset, please check our second stage dataset preparation instruction. To launch the second stage alignment, first specify the path to the checkpoint file trained in stage 1 in train_configs/minigpt4_stage1_pretrain.yaml. You can also specify the output path there. Then, run the following command. In our experiments, we use 1 A100.

torchrun --nproc-per-node NUM_GPU train.py --cfg-path train_configs/minigpt4_stage2_finetune.yaml

After the second stage alignment, MiniGPT-4 is able to talk about the image coherently and user-friendly.

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!

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

@misc{zhu2022minigpt4,
      title={MiniGPT-4: Enhancing Vision-language Understanding with Advanced Large Language Models}, 
      author={Deyao Zhu and Jun Chen and Xiaoqian Shen and xiang Li and Mohamed Elhoseiny},
      year={2023},
}

License

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