SpecFlow: Fast and Lossless Speculative Sampling for Flow Matching

Flow Matching (FM) has emerged as a leading framework for high-quality image and video generation, yet inference remains expensive because ODE integration requires sequential network evaluations. Existing acceleration methods either require additional training or rely on approximations, generally without exact distributional guarantees. We ask whether FM can instead admit the lossless acceleration of speculative decoding, where draft quality affects efficiency but not correctness. A direct extension is challenging because deterministic FM transitions are degenerate (Dirac) and lack the densities required for exact probabilistic verification. We address this by performing speculation on a marginal-preserving stochastic realization of the FM probability flow, whose Gaussian transitions enable exact verification and correction of draft proposals. We obtain the required score directly from the FM velocity, requiring no auxiliary model or additional network evaluations. Building on this, we introduce SpecFlow, a training-free speculative sampler that exactly reproduces its chosen discretized stochastic target sampler for any valid draft; making SpecFlow lossless. We further introduce a curvature-adaptive drafting strategy that jointly adapts prediction order and speculative horizon to improve efficiency. Across image generation, image editing, and video generation, SpecFlow achieves $>2.5\times$ inference speedup while maintaining the target sampler's quality.