Momentum Guidance: Plug-and-Play Guidance for Flow Models

Flow-based generative methods offer a simple and effectiveframework for high-fidelity generation, yet pretrained flow models arerarely used in their vanilla conditional form: in image generation, sam-ples without guidance often appear diffuse and lack fine-grained detail.Existing guidance techniques such as classifier-free guidance (CFG) im-prove fidelity but reduce sample diversity. We introduce MomentumGuidance (MG), a guidance method that improves sample quality byextrapolating the current velocity away from an exponential moving av-erage of past velocities along the ODE trajectory, while preserving thestandard one-evaluation-per-step cost. MG provides gains beyond CFG,improving the precision–recall Pareto frontier. Experiments demonstratethe effectiveness of MG across benchmarks. On ImageNet-256, MG im-proves FID by 36.54% without CFG and 25.42% with CFG on averageacross sampling settings, attaining an FID of 1.553 at 16 sampling steps.Evaluations on large flow-based models, including Stable Diffusion 3 andFLUX.1-dev, further confirm improvements across standard metrics.