Toward Efficient Consistency Models via Variance-Reduced Distillation.
This article introduces variance-reduced consistency learning (vrCL), a novel distillation technique that enables stable and efficient training of consistency models without relying on teacher model evaluations, resulting in high computational efficiency and significantly reduced training time.