By framing distribution matching as a classification problem with discriminators, DMAD eliminates the memory overhead of auxiliary models while maintaining or improving generation quality—enabling practical few-step visual generation.
DMAD improves fast image and video generation by training lightweight student models to match teacher distributions without needing an auxiliary model. It uses two discriminator heads to learn density ratios directly, making the process more efficient while achieving state-of-the-art quality in one-to-four-step generation across images and videos.