Balancing Act: Distribution-Guided Debiasing in Diffusion Models
Rishubh Parihar Abhijnya Bhat Saswat Mallick Abhipsa Basu 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 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 rebalancing the
training set with our generated data.