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新的截断跳跃采样（Truncated Jump Sampling）在无需重新训练的情况下加速了 AI 图像生成

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员引入了一种名为截断跳跃采样（Truncated Jump Sampling, TJS）的新颖方法，用于加速扩散模型和流匹配模型中的生成过程。该技术基于“端点可解码性”和“x-prediction”的概念，通过在更早的时间点停止 ODE 过程并解码干净样本来实现更快的采样。TJS 无需额外的训练或架构更改，在 SDXL 和 SD3.5M 等各种模型上显著减少了神经函数评估（NFEs），同时保持了接近匹配的质量。 AI

影响 加速了扩散模型和流匹配模型的推理速度，有可能降低计算成本并改善用户体验。

排序理由 该集群包含一篇详细介绍加速生成模型新方法的学术论文。

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

新的截断跳跃采样（Truncated Jump Sampling）在无需重新训练的情况下加速了 AI 图像生成

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该集群包含一篇详细介绍加速生成模型新方法的学术论文。
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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Xin Peng, Ang Gao ·

    x-Prediction Is All You Need：通过端点可解码性实现无需训练的加速生成

    arXiv:2607.06114v1 Announce Type: cross Abstract: Diffusion and flow matching models generate high-quality samples, but their ODE samplers often need tens to hundreds of neural function evaluations (NFEs). This remains a practical challenge for released checkpoints, since many ac…

  2. arXiv cs.AI TIER_1 English(EN) · Ang Gao ·

    x-Prediction is all you need：通过端点可解码性实现无需训练的加速生成

    Diffusion and flow matching models generate high-quality samples, but their ODE samplers often need tens to hundreds of neural function evaluations (NFEs). This remains a practical challenge for released checkpoints, since many accelerators require additional design choices and t…

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