以前的訓練方法是錯的？用REPA訓練效率提升17.5倍
擴散模型（Diffusion Model）作爲AI繪畫領域的"頂流"技術，一直以其卓越的生成效果備受矚目。然而，其漫長的訓練過程一直是制約其進一步發展的瓶頸。近日，一項名爲REPA（REPresentation Alignment）的創新技術爲解決這一問題帶來了突破性進展，有望將擴散模型的訓練效率提升17.5倍。擴散模型的核心原理是通過逐步向圖像添加噪聲，然後訓練模型反向還原出清晰圖像的過程。這種方法雖然效果顯著，但訓練過程耗時耗力，往往需要數百萬步的迭代才能達到理想效果。研究人員發現，這一問題的
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