通过实例感知离散化的少步扩散采样
Few-Step Diffusion Sampling Through Instance-Aware Discretizations
提出实例感知的离散化框架，根据输入自适应分配采样时间步，可即插即用于扩散/流匹配模型的少步采样，在像素空间、潜空间图像和视频生成中提升质量且推理开销可忽略。
原文依据 we propose an instance-aware discretization framework. Our method learns to adapt timestep allocations based on input-dependent priors
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论扩散模型和流匹配模型通过模拟由常微分方程或随机微分方程（ODEs/SDEs）定义的路径，从易于处理的先验分布开始生成高保真数据。概率流ODE的表述使得能够使用先进的数值求解器来加速采样。与求解器设计正交但同样关键的是离散化策略。早期方法采用手工设计的启发式规则，近期方法采用基于优化的技术，但大多数现有策略对所有样本强制使用全局共享的时间步调度。这种统一处理未能考虑生成过程中实例特定的复杂性，可能限制性能。受合成数据上控制实验的启发——该实验揭示了全局调度在实例特定动态下的次优性——我们提出了一个实例感知的离散化框架。我们的方法学习基于输入相关的先验来适应时间步分配，将基于梯度的离散化搜索扩展到条件生成设置。在多种设置下的实证结果，包括合成数据、像素空间扩散、潜空间图像和视频流匹配模型，表明与训练相比，我们的方法以边际调优成本持续提高生成质量，且推理开销可忽略不计。
英文原文摘要 1366 字符
Diffusion and flow matching models generate high-fidelity data by simulating paths defined by Ordinary or Stochastic Differential Equations (ODEs/SDEs), starting from a tractable prior distribution. The probability flow ODE formulation enables the use of advanced numerical solvers to accelerate sampling. Orthogonal yet vital to solver design is the discretization strategy. While early approaches employed handcrafted heuristics and recent methods adopt optimization-based techniques, most existing strategies enforce a globally shared timestep schedule across all samples. This uniform treatment fails to account for instance-specific complexity in the generative process, potentially limiting performance.Motivated by controlled experiments on synthetic data, which reveals the suboptimality of global schedules under instance-specific dynamics, we propose an instance-aware discretization framework. Our method learns to adapt timestep allocations based on input-dependent priors, extending gradient-based discretization search to the conditional generative setting. Empirical results across diverse settings, including synthetic data, pixel-space diffusion, latent-space images and video flow matching models, demonstrate that our method consistently improves generation quality with marginal tuning cost compared to training and negligible inference overhead.