Optimizing Negative Prompts for Enhanced Aesthetics and Fidelity in Text-To-Image Generation
<|reference_start|>Optimizing Negative Prompts for Enhanced Aesthetics and Fidelity in Text-To-Image Generation: In text-to-image generation, using negative prompts, which describe undesirable image characteristics, can significantly boost image quality. However, producing good negative prompts is manual and tedious. To address this, we propose NegOpt, a novel method for optimizing negative prompt generation toward enhanced image generation, using supervised fine-tuning and reinforcement learning. Our combined approach results in a substantial increase of 25% in Inception Score compared to other approaches and surpasses ground-truth negative prompts from the test set. Furthermore, with NegOpt we can preferentially optimize the metrics most important to us. Finally, we construct Negative Prompts DB (https://github.com/mikeogezi/negopt), a publicly available dataset of negative prompts.<|reference_end|>
@article{ogezi2024optimizing,
    title={Optimizing Negative Prompts for Enhanced Aesthetics and Fidelity in
  Text-To-Image Generation},
    author={Michael Ogezi and Ning Shi},
    journal={arXiv preprint arXiv:2403.07605},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.07605},
    primaryClass={cs.CV cs.AI cs.LG}
}