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

• URL: http://arxiv.org/abs/2602.20360v1
• Date: Mon, 23 Feb 2026 21:06:35 GMT
• Title: Momentum Guidance: Plug-and-Play Guidance for Flow Models
• Abstract summary: We introduce Momentum Guidance (MG), a new dimension of guidance that leverages the ODE trajectory itself.<n>MG extrapolates the current velocity using an exponential moving average of past velocities and preserves the standard one-evaluation-per-step cost.<n>MG achieves average improvements in FID of 36.68% without CFG and 25.52% with CFG across various sampling settings, attaining an FID of 1.597 at 64 sampling steps.
• Score: 11.47531308210865
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Flow-based generative models have become a strong framework for high-quality generative modeling, yet pretrained models are rarely used in their vanilla conditional form: conditional samples without guidance often appear diffuse and lack fine-grained detail due to the smoothing effects of neural networks. Existing guidance techniques such as classifier-free guidance (CFG) improve fidelity but double the inference cost and typically reduce sample diversity. We introduce Momentum Guidance (MG), a new dimension of guidance that leverages the ODE trajectory itself. MG extrapolates the current velocity using an exponential moving average of past velocities and preserves the standard one-evaluation-per-step cost. It matches the effect of standard guidance without extra computation and can further improve quality when combined with CFG. Experiments demonstrate MG's effectiveness across benchmarks. Specifically, on ImageNet-256, MG achieves average improvements in FID of 36.68% without CFG and 25.52% with CFG across various sampling settings, attaining an FID of 1.597 at 64 sampling steps. Evaluations on large flow-based models like Stable Diffusion 3 and FLUX.1-dev further confirm consistent quality enhancements across standard metrics.

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