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# MiniGPT-4: Enhancing Vision-language Understanding with Advanced Large Language Models |
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[Deyao Zhu](https://tsutikgiau.github.io/)* (On Job Market!), [Jun Chen](https://junchen14.github.io/)* (On Job Market!), [Xiaoqian Shen](https://xiaoqian-shen.github.io), [Xiang Li](https://xiangli.ac.cn), and [Mohamed Elhoseiny](https://www.mohamed-elhoseiny.com/). *Equal Contribution |
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**King Abdullah University of Science and Technology** |
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## Online Demo |
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Click the image to chat with MiniGPT-4 around your images |
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[![demo](figs/online_demo.png)](https://minigpt-4.github.io) |
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## Examples |
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![find wild](figs/examples/wop_2.png) | ![write story](figs/examples/ad_2.png) |
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![solve problem](figs/examples/fix_1.png) | ![write Poem](figs/examples/rhyme_1.png) |
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More examples can be found in the [project page](https://minigpt-4.github.io). |
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## Introduction |
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- MiniGPT-4 aligns a frozen visual encoder from BLIP-2 with a frozen LLM, Vicuna, using just one projection layer. |
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- 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. |
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- 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. |
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- 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. |
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- MiniGPT-4 yields many emerging vision-language capabilities similar to those demonstrated in GPT-4. |
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![overview](figs/overview.png) |
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## Getting Started |
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### Installation |
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**1. Prepare the code and the environment** |
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Git clone our repository, creating a python environment and ativate it via the following command |
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```bash |
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git clone https://github.com/Vision-CAIR/MiniGPT-4.git |
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cd MiniGPT-4 |
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conda env create -f environment.yml |
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conda activate minigpt4 |
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``` |
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**2. Prepare the pretrained Vicuna weights** |
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The current version of MiniGPT-4 is built on the v0 versoin of Vicuna-13B. |
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Please refer to our instruction [here](PrepareVicuna.md) |
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to prepare the Vicuna weights. |
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The final weights would be in a single folder with the following structure: |
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``` |
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vicuna_weights |
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βββ config.json |
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βββ generation_config.json |
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βββ pytorch_model.bin.index.json |
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βββ pytorch_model-00001-of-00003.bin |
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... |
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``` |
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Then, set the path to the vicuna weight in the model config file |
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[here](minigpt4/configs/models/minigpt4.yaml#L16) at Line 16. |
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**3. Prepare the pretrained MiniGPT-4 checkpoint** |
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To play with our pretrained model, download the pretrained checkpoint |
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[here](https://drive.google.com/file/d/1a4zLvaiDBr-36pasffmgpvH5P7CKmpze/view?usp=share_link). |
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Then, set the path to the pretrained checkpoint in the evaluation config file |
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in [eval_configs/minigpt4_eval.yaml](eval_configs/minigpt4_eval.yaml#L10) at Line 11. |
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### Launching Demo Locally |
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Try out our demo [demo.py](demo.py) on your local machine by running |
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``` |
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python demo.py --cfg-path eval_configs/minigpt4_eval.yaml --gpu-id 0 |
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``` |
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Here, we load Vicuna as 8 bit by default to save some GPU memory usage. |
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Besides, the default beam search width is 1. |
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Under this setting, the demo cost about 23G GPU memory. |
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If you have a more powerful GPU with larger GPU memory, you can run the model |
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in 16 bit by setting low_resource to False in the config file |
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[minigpt4_eval.yaml](eval_configs/minigpt4_eval.yaml) and use a larger beam search width. |
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### Training |
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The training of MiniGPT-4 contains two alignment stages. |
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**1. First pretraining stage** |
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In the first pretrained stage, the model is trained using image-text pairs from Laion and CC datasets |
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to align the vision and language model. To download and prepare the datasets, please check |
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our [first stage dataset preparation instruction](dataset/README_1_STAGE.md). |
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After the first stage, the visual features are mapped and can be understood by the language |
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model. |
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To launch the first stage training, run the following command. In our experiments, we use 4 A100. |
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You can change the save path in the config file |
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[train_configs/minigpt4_stage1_pretrain.yaml](train_configs/minigpt4_stage1_pretrain.yaml) |
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```bash |
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torchrun --nproc-per-node NUM_GPU train.py --cfg-path train_configs/minigpt4_stage1_pretrain.yaml |
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``` |
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A MiniGPT-4 checkpoint with only stage one training can be downloaded |
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[here](https://drive.google.com/file/d/1u9FRRBB3VovP1HxCAlpD9Lw4t4P6-Yq8/view?usp=share_link). |
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Compared to the model after stage two, this checkpoint generate incomplete and repeated sentences frequently. |
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**2. Second finetuning stage** |
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In the second stage, we use a small high quality image-text pair dataset created by ourselves |
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and convert it to a conversation format to further align MiniGPT-4. |
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To download and prepare our second stage dataset, please check our |
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[second stage dataset preparation instruction](dataset/README_2_STAGE.md). |
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To launch the second stage alignment, |
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first specify the path to the checkpoint file trained in stage 1 in |
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[train_configs/minigpt4_stage1_pretrain.yaml](train_configs/minigpt4_stage2_finetune.yaml). |
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You can also specify the output path there. |
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Then, run the following command. In our experiments, we use 1 A100. |
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```bash |
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torchrun --nproc-per-node NUM_GPU train.py --cfg-path train_configs/minigpt4_stage2_finetune.yaml |
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``` |
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After the second stage alignment, MiniGPT-4 is able to talk about the image coherently and user-friendly. |
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## Acknowledgement |
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+ [BLIP2](https://huggingface.co/docs/transformers/main/model_doc/blip-2) 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! |
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+ [Lavis](https://github.com/salesforce/LAVIS) This repository is built upon Lavis! |
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+ [Vicuna](https://github.com/lm-sys/FastChat) The fantastic language ability of Vicuna with only 13B parameters is just amazing. And it is open-source! |
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If you're using MiniGPT-4 in your research or applications, please cite using this BibTeX: |
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```bibtex |
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@misc{zhu2022minigpt4, |
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title={MiniGPT-4: Enhancing Vision-language Understanding with Advanced Large Language Models}, |
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author={Deyao Zhu and Jun Chen and Xiaoqian Shen and xiang Li and Mohamed Elhoseiny}, |
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year={2023}, |
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} |
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``` |
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## License |
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This repository is under [BSD 3-Clause License](LICENSE.md). |
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Many codes are based on [Lavis](https://github.com/salesforce/LAVIS) with |
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BSD 3-Clause License [here](LICENSE_Lavis.md). |