Adapts temporal priors from video models to conditional image generation, selecting transformation scales based on each input. A latent intermediate state guides how condition information reaches the target, with evaluations spanning subject generation, edge-to-image synthesis, and editing.
Improves few-step text-to-image distillation by matching feature means and per-coordinate standard deviations. It retains DMD’s full score correction and uses the generator’s EMA copy for features, avoiding an external encoder.