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arXiv · 2403.07605

Optimizing Negative Prompts for Enhanced Aesthetics and Fidelity in Text-To-Image Generation

Michael Ogezi·Ning Shi
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Abstract

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://huggingface.co/datasets/mikeogezi/negopt_full), a publicly available dataset of negative prompts.

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BibTeXRIS

Michael Ogezi, Ning Shi. 2024-03-12. Optimizing Negative Prompts for Enhanced Aesthetics and Fidelity in Text-To-Image Generation. https://arxiv.org/abs/2403.07605

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