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arxiv:2403.07605

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

Published on Mar 12
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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, a dataset of negative prompts.

Community

Hey Michael. Are you guys planning to open source the code and DB so we can take this for a spin?

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Paper author

Hi, Andu. I believe we've spoken about the dataset via other channels, but this version should be easier to work with: https://huggingface.co/datasets/mikeogezi/negopt_full.

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