The rapid adoption of text-to-image (T2I) diffusion models in society underscores an urgent need to address their biases. Without interventions, these biases propagate a distorted worldview and limit opportunities for minority groups. In this work, we frame fairness as a distributional alignment problem. We propose to end-to-end finetune diffusion models using a distributional alignment loss, steering specific characteristics of the generated images towards a user-defined target distribution. Empirically, our method markedly reduces gender, racial, and their intersectional biases for occupational prompts. Gender bias can be substantially mitigated even when finetuning merely five soft tokens. Acknowledging strict egalitarianism might not always be the desired outcome for fairness, we show that our method can flexibly control age to a $75\\%$ young and $25\\%$ old distribution while simultaneously debiasing gender and race. Finally, our method is scalable: it can debias multiple concepts at once, such as occupations, sports, and personal descriptors, by simply including these prompts in the finetuning data. We hope our work facilitates the advancement of social alignment for T2I generative AI. We will share code and various debiased diffusion model adaptors.
Finetuning Text-to-Image Diffusion Models for Fairness
Xudong Shen Chao Du Tianyu Pang Min Lin Yongkang Wong Mohan Kankanhalli