LISA Regularization Improves Side-Network Controllable Image Generation Training

LISA reframes the dual-branch controllable generation paradigm using score-based modeling: the frozen main network provides an unconditional prior score while the side network contributes a likelihood score. Explicit alignment of these scores via regularization improves training efficiency and conditional control quality.