GENIE: Socially Unbiased Generative Text-to-Image Editing
Julia Kaiwen Lau, Raphaël C.-W. Phan, Sailaja Rajanala, Ingemar J. Cox, Arghya Pal
Abstract
Generative diffusion models often exhibit societal biases in sensitive personal attributes such as age, gender, and race. In this work, we describe GENIE – a method to reduce such biases in a variety of classifier-free diffusion models used for image editing. Our method implicitly incorporates debiasing terms together with the user’s explicit edit instruction to reduce bias. This automatic method relieves the user from needing to modify edit instructions in order to avoid bias. Further, no additional training is needed. Experimental results are provided based on modifications to four diffusion models, namely InstructPix2Pix, Stable Diffusion 1.5, Stable Diffusion 2.1, and Stable Diffusion XL. We show that, on average, bias is reduced by 31% in gender, 15% in age, 39% in race.
BibTeX
@inproceedings{icassp2025_geniesociallyunb,
title = {GENIE: Socially Unbiased Generative Text-to-Image Editing},
author = {Julia Kaiwen Lau and Raphaël C.-W. Phan and Sailaja Rajanala and Ingemar J. Cox and Arghya Pal},
booktitle = {ICASSP 2025},
year = {2025}
}