Sharpen Your Flow: Sharpness-Aware Sampling for Flow Matching

• URL: http://arxiv.org/abs/2605.11547v1
• Date: Tue, 12 May 2026 05:22:54 GMT
• Title: Sharpen Your Flow: Sharpness-Aware Sampling for Flow Matching
• Abstract summary: Flow matching models generate samples by numerically integrating a learned velocity field.<n>Key question is not only how to integrate the flow, but where the sampler should spend its steps.<n>We propose SharpEuler, a training-free sampler that profiles a pretrained model offline.
• Score: 14.19197444541245
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Flow matching models generate samples by numerically integrating a learned velocity field, with each integration step requiring a neural network evaluation. Fast generation therefore requires using a small fixed evaluation budget effectively: the key question is not only how to integrate the flow, but where the sampler should spend its steps. We propose SharpEuler, a training-free sampler that profiles a pretrained model offline by estimating where the learned velocity field changes most rapidly along calibration trajectories. This finite-difference estimate defines a solver-aware sharpness profile, which is smoothed and converted by a quantile transform into a timestep grid for any desired inference budget. At test time, sampling remains ordinary Euler integration with the same number of model evaluations as a uniform schedule. We justify SharpEuler using three principles: a numerical principle identifying trajectory acceleration as the leading source of Euler discretization error, a variational principle deriving sharpness-based power-law timestep densities, and a statistical guarantee showing that the finite-sample calibrated sampler is stable at the terminal distribution level. Our experiments show that SharpEuler improves sample quality at fixed budgets, reducing inter-mode leakage and increasing mode coverage.

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