以前的训练方法是错的？用REPA训练效率提升17.5倍
扩散模型（Diffusion Model）作为AI绘画领域的"顶流"技术，一直以其卓越的生成效果备受瞩目。然而，其漫长的训练过程一直是制约其进一步发展的瓶颈。近日，一项名为REPA（REPresentation Alignment）的创新技术为解决这一问题带来了突破性进展，有望将扩散模型的训练效率提升17.5倍。扩散模型的核心原理是通过逐步向图像添加噪声，然后训练模型反向还原出清晰图像的过程。这种方法虽然效果显著，但训练过程耗时耗力，往往需要数百万步的迭代才能达到理想效果。研究人员发现，这一问题的
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