Researchers propose Distribution Matching Distillation for text-to-image generation

Distribution Matching Distillation (DMD) uses score differences to bring the generated distribution closer to a reference distribution and estimates this difference with internal velocity branches, avoiding a separate fake-score network. The authors test whether adding an explicit moment constraint improves the learned score supervision.