ArtGPT-4: Artistic Vision-Language Understanding with Adapter-enhanced MiniGPT-4

Online Demo

Waiting for updates...

Examples

More examples can be found in the project page.

Introduction

• ArtGPT-4 is a novel model that builds upon the architecture of MiniGPT-4 by incorporating tailored linear layers and activation functions into Vicuna, specifically designed to optimize the model's performance in vision-language tasks.
• The modifications made to Vicuna in ArtGPT-4 enable the model to better capture intricate details and understand the meaning of artistic images, resulting in improved image understanding compared to the original MiniGPT-4 model.
• 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 (5000 pairs in total) yet high-quality dataset.
• ArtGPT-4 was trained using about 200 GB of image-text pairs on a Tesla A100 device in just 2 hours, demonstrating impressive efficiency and effectiveness in training.
• In addition to improved image understanding, ArtGPT-4 is capable of generating visual code, including aesthetically pleasing HTML/CSS web pages, with a more artistic flair.

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/artgpt4_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.

License

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