标题： 扩散模型中的对抗噪声 显示英文标题

标题： Antithetic Noise in Diffusion Models

摘要： 我们系统地研究了扩散模型中的对抗性初始噪声。在针对不同数据集训练的无条件模型、文本条件潜扩散模型以及扩散后验采样器中，我们发现将每个初始噪声与其否定配对始终会产生强负相关的样本。为了解释这一现象，我们将实验与理论分析相结合，提出了一个关于学习得分函数近似仿射反对称（具有常数偏移的奇对称）的猜想，并提供了支持该猜想的证据。利用这种负相关性，我们实现了两种应用：（1）在不影响质量的情况下增强像Stable Diffusion这类模型的图像多样性；（2）当估计下游统计量时，提高不确定性量化精度（例如，置信区间可缩小高达90%）。在此基础上，我们将两点配对扩展到随机化准蒙特卡洛估计器中，进一步提高了估计准确性。我们的框架无需额外训练，适用于各种模型且不会增加运行时开销。 显示英文摘要

摘要： We initiate a systematic study of antithetic initial noise in diffusion models. Across unconditional models trained on diverse datasets, text-conditioned latent-diffusion models, and diffusion-posterior samplers, we find that pairing each initial noise with its negation consistently yields strongly negatively correlated samples. To explain this phenomenon, we combine experiments and theoretical analysis, leading to a symmetry conjecture that the learned score function is approximately affine antisymmetric (odd symmetry up to a constant shift), and provide evidence supporting it. Leveraging this negative correlation, we enable two applications: (1) enhancing image diversity in models like Stable Diffusion without quality loss, and (2) sharpening uncertainty quantification (e.g., up to 90% narrower confidence intervals) when estimating downstream statistics. Building on these gains, we extend the two-point pairing to a randomized quasi-Monte Carlo estimator, which further improves estimation accuracy. Our framework is training-free, model-agnostic, and adds no runtime overhead.