Mitigating Intersectional Bias in Text-to-Image Diffusion Models through Disentangled Cross-Attention Editing

The authors propose a novel method, MIST, that mitigates intersectional biases in text-to-image diffusion models by fine-tuning the cross-attention layers in a disentangled manner, without the need for retraining or manually curated reference image sets.