Optimizing Negative Prompts for Enhanced Aesthetics and Fidelity in
  Text-To-Image Generation
Michael Ogezi Ning Shi
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, a dataset of negative prompts.