📰 2026-05-07 04:00 更新
🔸 Learning the Integral of a Diffusion Model / 学习扩散模型的积分
🔗 Learning the Integral of a Diffusion Model
🔥 18 points
原文:
Sampling from a diffusion model is an iterative process: at each step, the denoiser estimates the tangent direction to a path through input space. We move along this path by repeatedly taking small steps in this direction, effectively calculating an integral across noise levels. This gradually transforms samples from a simple noise distribution into samples from a target distribution, and traces out the path that connects them. Can we train neural networks to directly predict this integral in…
译文:
来自扩散模型的采样是一个迭代过程：在每个步骤中，去噪器估计通过输入空间的路径的正切方向。我们沿着这条道路前进，反复在这个方向上迈出小步，有效地计算噪声水平的积分。这逐渐将样本从简单的噪声分布转换为目标分布的样本，并追踪连接它们的路径。我们可以训练Neura吗 l网络直接预测这个积分在…
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