Visual Instruction Inversion: Image Editing via Image Prompting
Thao Nguyen, Yuheng Li, Utkarsh Ojha, Yong Jae Lee
Abstract
Text-conditioned image editing has emerged as a powerful tool for editing images. However, in many situations, language can be ambiguous and ineffective in describing specific image edits. When faced with such challenges, visual prompts can be a more informative and intuitive way to convey ideas. We present a method for image editing via visual prompting. Given pairs of example that represent the "before" and "after" images of an edit, our goal is to learn a text-based editing direction that can be used to perform the same edit on new images. We leverage the rich, pretrained editing capabilities of text-to-image diffusion models by inverting visual prompts into editing instructions. Our results show that with just one example pair, we can achieve competitive results compared to state-of-the-art text-conditioned image editing frameworks.
BibTeX
@inproceedings{
nguyen2023visual,
title={Visual Instruction Inversion: Image Editing via Image Prompting},
author={Thao Nguyen and Yuheng Li and Utkarsh Ojha and Yong Jae Lee},
booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
year={2023},
url={https://openreview.net/forum?id=l9BsCh8ikK}
}