• fine-tuning — Taking a trained model and training it a bit more on your own examples so it gets better at one specific job.

The approach offers a concrete recipe for debiasing text-to-image models using synthetic data built from combinatorial prompt attributes, with released prompts and code and measured group fairness gains on both SD1.5 and SDXL.

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