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Apple发布归一化轨迹模型，加速AI图像生成

作者 PulseAugur 编辑部 · [1 个来源] ·

Apple Machine Learning Research推出了归一化轨迹模型（NTM），这是一种新颖的生成模型方法，即使只有少数粗略的采样步骤也能保持精确的似然训练。与以往牺牲似然性换取速度的方法不同，NTM在每个反向步骤中利用了表达性条件归一化流。这种架构结合了浅层可逆块和深度并行预测器，允许从头开始训练或从预训练的流匹配模型进行初始化。NTM在文本到图像基准测试中表现出竞争力，仅用四个步骤即可生成高质量样本，同时保持生成轨迹的精确似然。 AI

影响 这项研究通过减少所需的采样步骤，有望带来更高效、更快速的AI图像生成模型。

排序理由 该集群描述了一篇详细介绍主要科技公司研究部门一项新颖建模技术的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 Apple Machine Learning Research 阅读 →

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Apple发布归一化轨迹模型，加速AI图像生成

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该集群描述了一篇详细介绍主要科技公司研究部门一项新颖建模技术的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. Apple Machine Learning Research TIER_1 English(EN) ·

    归一化轨迹模型

    Diffusion-based models decompose sampling into many small Gaussian denoising steps, an assumption that breaks down when generation is compressed to a few coarse transitions. Existing few-step methods address this through distillation, consistency training, or adversarial objectiv…

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