AI RESEARCH
Stay Fair! Ensuring Group Fairness in Diffusion Models Across Guidance Scales
arXiv CS.LG
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ArXi:2605.28036v1 Announce Type: cross Diffusion models steer conditional generation with a tunable guidance scale to trade off prompt alignment and diversity. However, existing debiasing techniques are optimized for a single scale, degrading fairness when users adjust this parameter. We trace this behavior to a previously overlooked source by decomposing total bias into two components: a model bias and a guidance bias.