arxiv preprint – Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Think

In this episode, we discuss Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Think by Sihyun Yu, Sangkyung Kwak, Huiwon Jang, Jongheon Jeong, Jonathan Huang, Jinwoo Shin, Saining Xie. The paper presents a novel approach called REPresentation Alignment (REPA) to enhance the training efficiency and quality of generative diffusion models by integrating high-quality external visual representations. This method aligns noisy input states with clean image representations from pretrained visual encoders, leading to significantly faster training times—up to 17.5 times faster—and improved generation quality. The results demonstrate that REPA achieves state-of-the-art generation quality using classifier-free guidance compared to traditional methods.