FlowBudget：流匹配模型的无训练采样预算估计
原标题：FlowBudget: Training-Free Sampling-Budget Estimation for Flow Matching Models
AI 摘要
该论文提出 FlowBudget，一种无需训练、无需生成样本的采样预算估计方法，用于流匹配模型。它通过模拟无关的插值探针估计数值离散化误差系数，结合求解器阶数将数值容差映射为所需的函数评估次数（NFE）预算。在 SiT、FlowDCN 和 Rectified Flow 模型上的实验表明，该方法无需进行 FID 与 NFE 的扫描即可估计采样预算。
正文节选
FLOWBUDGET: TRAINING-FREE SAMPLING-BUDGET ESTIMATION FOR FLOW MATCHING MODELS Abstract Sampling from continuous flow-based generators such as flow matching models requires numerical ODE or SDE integration, making inference cost depend on the number of function evaluations (NFEs). In practice, the sampling budget is inherited from prior work or selected through expensive FID-versus-NFE sweeps, using FID to assess image-generation quality, requiring sample generation and evaluation at multiple bud