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English(EN) ExploreNet: Learning Where to Explore in Diffusion GRPO

ExploreNet 通过学习自适应噪声尺度来增强扩散模型

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了 ExploreNet，这是一种新颖的策略，旨在通过在扩散模型中学习自适应探索分布来改进图像生成。与之前应用均匀噪声的方法不同，ExploreNet 根据当前的潜在表示、去噪步骤和提示来预测每个潜在元素的噪声尺度。这种方法在 Stable Diffusion 3.5 Medium 上进行了测试，在 GenEval2 等基准测试中显著提高了性能，并取得了高人类偏好胜率，证明了探索分布的形状比其幅度更关键。 AI

影响 引入了一种用于扩散模型自适应探索的新颖技术，有望提高图像生成质量和效率。

排序理由 详细介绍一种改进扩散模型新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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ExploreNet 通过学习自适应噪声尺度来增强扩散模型

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详细介绍一种改进扩散模型新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Shuyue Stella Li, Xiaochuang Han, Yulia Tsvetkov, Luke Zettlemoyer ·

    ExploreNet：学习在 Diffusion GRPO 中探索何处

    arXiv:2609.38329v1 Announce Type: new Abstract: Group-relative RL methods such as Flow-GRPO post-train image generators by exploring with isotropic Gaussian noise added at every denoising step. This noise decides which rollouts the model learns from, yet it perturbs every channel…

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