研究人员通过将扩散模型和自回归（AR）模型概念化为腐化格上的路径，实现了两者的统一。该框架允许根据数据几何（特别是图的树深度）来预测解码计划的性能，树深度决定了零成本计划的最小步数。该研究提出了一种通过估计预训练权重的成对依赖性来对计划进行排序的方法，并在文本、图像和视频生成任务中验证了这些预测。 AI
影响 为未来AR模型、扩散模型和混合模型的解码计划提供了统一的设计原则。
排序理由 该集群包含一篇详细介绍生成模型新理论框架的学术论文。
在 Hugging Face Daily Papers 阅读 →
AI 生成摘要 · Google Gemini · 来自 3 个来源。 我们如何撰写摘要 →
完整方法见我们的编辑标准。
arXiv:2610.11216v1 Announce Type: cross Abstract: Diffusion and autoregression (AR) have long been seen as different categories of generative models, with diffusion specialising in continuous fields and AR specialising in discrete tokens. Recent work seeks to combine the advantag…
Diffusion and autoregression (AR) have long been seen as different categories of generative models, with diffusion specialising in continuous fields and AR specialising in discrete tokens. Recent work seeks to combine the advantages of the two models, and each hybrid fixes its de…
Diffusion and autoregression (AR) have long been seen as different categories of generative models, with diffusion specialising in continuous fields and AR specialising in discrete tokens. Recent work seeks to combine the advantages of the two models, and each hybrid fixes its de…