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/DLYuanGod/ArtGPT-4.git
cd ArtGPT-4
conda env create -f environment.yml
conda activate artgpt4
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 in a structure similar to the following:
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 Downlad
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 --gpu-id 0
Training
The training of ArtGPT-4 contains two alignment stages. The training process for the step is consistent with that of MiniGPT-4.
Datasets We use Laion-aesthetic from the LAION-5B dataset, which amounts to approximately 200GB for the first 302 tar files.
Acknowledgement
- MiniGPT-4 Our work is based on improvements to the model.
If you're using ArtGPT-4 in your research or applications, please cite using this BibTeX:
@article{yuan2023artgpt4,
title={ArtGPT-4: Artistic Vision-Language Understanding with Adapter-enhanced MiniGPT-4},
author={Yuan, Zhengqng and Xue, Huiwen and Wang, Xinyi and Liu, Yongming and Zhao, Zhuanzhe and Wang, Kun},
year={2023}
}
License
This repository is under BSD 3-Clause License. Many codes are based on Lavis with BSD 3-Clause License here.