Stay Fair! Ensuring Group Fairness in Diffusion Models Across Guidance Scales

arXiv:2605.28036v2 Announce Type: replace-cross Abstract: 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. While prior work primarily targets the former, we show that the guidance bias varies with the guidance scale, persisting even in models that have already been debiased. To address this, we extend Strong Demographic Parity to guidance and derive a condition under which the target distribution retains its group ratio across guidance scales. Building on this condition, we propose StayFair, which designs fair guidance algorithms in both regimes. For classifier guidance, it equalizes the classifier's output distributions across groups; for classifier-free guidance, it shifts the null embedding by a prompt-dependent offset. Because StayFair modifies only the guidance step, it is orthogonal to model debiasing and can be layered onto existing fair diffusion models. Across class-conditional and text-to-image generation, StayFair decouples fairness from the guidance scale without sacrificing image quality. Our decomposition also enables a more detailed analysis of the sources of bias in diffusion models.