## 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 ```bash 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](PrepareVicuna.md) 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](minigpt4/configs/models/minigpt4.yaml#L16) at Line 16. **3. Prepare the pretrained ArtGPT-4 checkpoint** [Downlad](https://huggingface.co/Tyrannosaurus/ArtGPT-4/blob/main/ArtGPT-4.pth) Then, set the path to the pretrained checkpoint in the evaluation config file in [eval_configs/minigpt4_eval.yaml](eval_configs/minigpt4_eval.yaml#L10) at Line 11. ### Launching Demo Locally Try out our demo [demo.py](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](https://minigpt-4.github.io/). **Datasets** We use [Laion-aesthetic](https://github.com/LAION-AI/laion-datasets/blob/main/laion-aesthetic.md) from the LAION-5B dataset, which amounts to approximately 200GB for the first 302 tar files. ## Acknowledgement + [MiniGPT-4](https://minigpt-4.github.io/) Our work is based on improvements to the model. ## License This repository is under [BSD 3-Clause License](LICENSE.md). Many codes are based on [Lavis](https://github.com/salesforce/LAVIS) with BSD 3-Clause License [here](LICENSE_Lavis.md).