通过恒定分布变化率优化扩散模型噪声调度，提升生成质量
Constant Rate Scheduling: A General Framework for Optimizing Diffusion Noise Schedule via Distributional Change
- 以用户定义的差异度量控制扩散过程中的分布变化速率
- 在多种数据集和采样器上实现稳定性能提升，最高达FID 2.03
- 适用于像素与潜在空间模型，兼顾生成质量和模式覆盖
我们提出一种通用框架，用于优化扩散模型的噪声调度，适用于训练和采样阶段。该方法在扩散过程中强制保持数据分布变化的恒定速率，速率由用户定义的差异度量来量化。我们引入了三种此类度量，可根据领域和模型架构灵活选择或组合。尽管本框架受理论洞察启发，但并未试图提供分布变化对样本质量影响的完整理论解释，而是专注于建立一个通用调度框架并验证其经验有效性。通过大量实验，我们证明该方法在多种数据集、采样器及函数评估次数（5至250）下均能持续提升像素空间与潜在空间扩散模型的性能。尤其在同时优化训练与采样调度时，该方法在LSUN Horse 256×256数据集上达到2.03的FID分数，且不牺牲模式覆盖率。
原文摘要 · Abstract (English)
We propose a general framework for optimizing noise schedules in diffusion models, applicable to both training and sampling. Our method enforces a constant rate of change in the probability distribution of diffused data throughout the diffusion process, where the rate of change is quantified using a user-defined discrepancy measure. We introduce three such measures, which can be flexibly selected or combined depending on the domain and model architecture. While our framework is inspired by theoretical insights, we do not aim to provide a complete theoretical justification of how distributional change affects sample quality. Instead, we focus on establishing a general-purpose scheduling framework and validating its empirical effectiveness. Through extensive experiments, we demonstrate that our approach consistently improves the performance of both pixel-space and latent-space diffusion models, across various datasets, samplers, and a wide range of number of function evaluations from 5 to 250. In particular, when applied to both training and sampling schedules, our method achieves a state-of-the-art FID score of 2.03 on LSUN Horse 256$\times$256, without compromising mode coverage.
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