MIST: Mitigating Intersectional Bias with Disentangled Cross-Attention Editing in Text-to-Image Diffusion Models

arXiv:2403.19738v1 Announce Type: new
Abstract: Diffusion-based text-to-image models have rapidly gained popularity for their ability to generate detailed and realistic images from textual descriptions. However, these models often reflect the biases present in their training data, especially impacting marginalized groups. While prior efforts to debias language models have focused on addressing specific biases, such as racial or gender biases, efforts to tackle intersectional bias have been limited. Intersectional bias refers to the unique form of bias experienced by individuals at …

abstract arxiv attention bias biases cs.cv data diffusion diffusion models editing generate however image image diffusion images language language models prior text text-to-image textual training training data type