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新JAGG方法将扩散模型训练速度提高2倍

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一种名为Jacobian-Aggregated Group Gradient (JAGG)的新技术，以显著加快强化学习任务中扩散模型的训练速度。目前的方法在每个采样步骤中通过高容量的Diffusion Transformer (DiT)骨干网络反向传播梯度时面临计算瓶颈，导致高分辨率图像生成的训练成本高昂。JAGG通过近似中间雅可比矩阵并聚合梯度，将反向传播次数减少了约一半，同时保持了可忽略的质量损失，这在文本到图像基准测试中得到了证明。 AI

影响 该方法可以显著降低扩散模型训练的计算成本，有望加速生成式AI在图像和其他模态方面的研究和开发。

排序理由 该集群描述了arXiv论文中提出的一种新方法，用于提高扩散模型训练的效率。

在 Hugging Face Daily Papers 阅读 →

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新JAGG方法将扩散模型训练速度提高2倍

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该集群描述了arXiv论文中提出的一种新方法，用于提高扩散模型训练的效率。
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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Ruiyi Ding, Jie Li, He Kang, Ziyan Liu, Chengru Song, Yuan chen ·

    AGG：用于扩散模型高效 GRPO 训练的雅可比聚合组梯度

    arXiv:2607.17572v1 Announce Type: new Abstract: Group Relative Policy Optimization (GRPO) is a powerful reinforcement learning algorithm for aligning generative models with human preferences. While successful in large language models~\cite{shao2024deepseekmathpushinglimitsmathema…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    AGG：用于扩散模型高效 GRPO 训练的雅可比聚合组梯度

    Group Relative Policy Optimization (GRPO) is a powerful reinforcement learning algorithm for aligning generative models with human preferences. While successful in large language models~\cite{shao2024deepseekmathpushinglimitsmathematical}, its extension to diffusion and flow matc…

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