Thanks for your interest in our work, ORES: Open-vocabulary Responsible Visual Synthesis. We release our code, checkpoint, and dataset here.
[News🤩] ORES has been accepted by AAAI 2024!
@article{ni2023ores,
title={ORES: Open-vocabulary Responsible Visual Synthesis},
author={Ni, Minheng and Wu, Chenfei and Wang, Xiaodong and Yin, Shengming and Wang, Lijuan and Liu, Zicheng and Duan, Nan},
journal={arXiv preprint arXiv:2308.13785},
year={2023}
}
We follow Stable Diffusion to set up environment.yaml.
conda env create -f environment.yaml
conda activate ldm
We design a WebUI for an out-of-the-box start. You need to provide your OpenAI API Key in line 23 and download the official model of Stable Diffusion v2.1 from this url to TIN/checkpoints/v2-1_768-ema-pruned.ckpt.
To start up WebUI, use
cd TIN
python ui.py
We also provide the training code for Learnable Instruction. You need to provide your OpenAI API Key in line 9.
To start training, use
cd Instruction
python instruction.py
We release this dataset under an MIT license. Refer to Dataset/ORES_data.txt for details.
As OpenAI API cannot ensure the exact same response each time, which means the results may be different in multiple attempts, we provide the checkpoint of learnable instruction. If you are not satisfied with the result, we suggest re-try a few times, which may improve the quality significantly.