FlowBudget: A Training-Free Approach for Estimating Sampling Budgets in Flow Matching Models
Summary:FlowBudget introduces a novel training-free approach for estimating the sampling complexity of flow matching models. By utilizing simulation-free interpolation probes of the learned dynamics, it normalizes the leading one-step discretization error by motion and maps numerical tolerance to step budget using the solver order. This method avoids the costly process of FID-versus-NFE sweeps and sample generation at multiple budgets. Experiments across various flow-model architectures demonstrate its
Core Breakthrough
FlowBudget is an innovative method for estimating the sampling complexity of flow-matching models without training. Its key mechanisms include:
- Simulation-free interpolation probes: By analyzing the interpolation probes of the learned dynamics, FlowBudget captures the numerical error of the model during inference.
- Normalization and coefficient estimation: It normalizes the leading one-step discretization error by motion and estimates its coefficient using interpolation probes.
- Numerical tolerance mapping: Using the solver order, it maps the numerical tolerance to a step budget, determining the required number of function evaluations (NFE).
Technical Mechanism Analysis
FlowBudget derives estimators for ODE sampling (e.g., Euler and Heun methods) and SDE sampling (e.g., additive-noise Euler-Maruyama method) and connects them to the terminal integration error. Its main advantages are:
- Training-free: No additional training of the model is required for budget estimation, reducing computational costs.
- Efficiency: It avoids the costly process of FID-versus-NFE sweeps, making it more efficient.
- Versatility: Applicable to various flow model architectures and formulations.
Engineering Trade-offs and Experimental Performance
Experiments on SiT, FlowDCN, and Rectified Flow models demonstrate that FlowBudget can accurately estimate the NFE budget without relying on repeated empirical evaluation. Specifically:
- Accuracy: The estimation results of FlowBudget are highly consistent with the actual required NFE in multiple benchmark tests.
- Efficiency: It significantly reduces the computational overhead during inference, improving overall efficiency.
Developer Implementation and Deployment Recommendations
FlowBudget provides developers with an efficient tool for optimizing the inference efficiency of flow-matching models. Here are some recommendations:
- Resource-constrained scenarios: In resource-constrained devices, FlowBudget can help developers determine the optimal NFE budget to balance inference speed and accuracy.
- Dynamic adjustment: Depending on different application scenarios, developers can dynamically adjust the estimation parameters of FlowBudget to meet varying accuracy requirements.
Conclusion
FlowBudget offers an innovative solution for optimizing the inference efficiency of flow-matching models. Its training-free, efficient, and versatile characteristics make it an important tool for flow model developers.
— END —Source: ArXiv Machine Learning (cs.LG) (2026-10-16)
Tags: #FlowBudget #Flow Matching Models #Inference Optimization
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