Balancing Act: Distribution-Guided Debiasing in Diffusion Models
Authors: Rishubh Parihar, Abhijnya Bhat, Abhipsa Basu, Saswat Mallick, Jogendra Nath Kundu, R. Venkatesh Babu

Diffusion Models (DMs) have emerged as powerful generative models with unprecedented image generation capability. These models are widely used for data augmentation and creative applications. However DMs reflect the biases present in the training datasets. This is especially concerning in the context of faces where the DM prefers one demographic subgroup vs others (eg. female vs male). In this work we present a method for debiasing DMs without relying on additional reference data or model retraining. Specifically we propose Distribution Guidance which enforces the generated images to follow the prescribed attribute distribution. To realize this we build on the key insight that the latent features of denoising UNet hold rich demographic semantics and the same can be leveraged to guide debiased generation. We train Attribute Distribution Predictor (ADP) - a small mlp that maps the latent features to the distribution of attributes. ADP is trained with pseudo labels generated from existing attribute classifiers. The proposed Distribution Guidance with ADP enables us to do fair generation. Our method reduces bias across single/multiple attributes and outperforms the baseline by a significant margin for unconditional and text-conditional diffusion models. Further we present a downstream task of training a fair attribute classifier by augmenting the training set with our generated data.