LISA: Likelihood Score Alignment for Visual-condition Controllable Generation
LISA introduces Likelihood Score Alignment, a regularization method that explicitly aligns side network features with an approximated likelihood score. This technique reframes the dual-branch paradigm by decomposing conditional scores, forcing the side network to learn its role more effectively. It significantly accelerates training and improves generation quality across various visual tasks.