High-fidelity text-to-image and text-to-video generation typically relies on Classifier-Free Guidance (CFG), but achieving optimal results often demands computationally expensive sampling schedules. In this work, we propose MAMBO-G, a training-free acceleration framework that significantly reduces computational cost by dynamically optimizing guidance magnitudes. We observe that standard CFG schedules are inefficient, applying disproportionately large updates in early steps that hinder convergence speed. MAMBO-G mitigates this by modulating the guidance scale based on the update-to-prediction magnitude ratio, effectively stabilizing the trajectory and enabling rapid convergence. This efficiency is particularly vital for resource-intensive tasks like video generation. Our method serves as a universal plug-and-play accelerator, achieving up to 3x speedup on Stable Diffusion v3.5 (SD3.5) and 4x on Lumina. Most notably, MAMBO-G accelerates the 14B-parameter Wan2.1 video model by 2x while preserving visual fidelity, offering a practical solution for efficient large-scale video synthesis. Our implementation follows a mainstream open-source diffusion framework and is plug-and-play with existing pipelines.

翻译：高保真度的文本到图像与文本到视频生成通常依赖于无分类器引导（CFG），但获得最优结果往往需要计算成本高昂的采样调度。在本工作中，我们提出了MAMBO-G，一种免训练的加速框架，通过动态优化引导幅度显著降低计算成本。我们观察到，标准的CFG调度效率低下，其在早期步骤中应用了不成比例的大更新，从而阻碍了收敛速度。MAMBO-G通过基于更新与预测的幅度比率来调制引导尺度，有效地稳定了轨迹并实现了快速收敛，缓解了这一问题。这种效率对于视频生成等资源密集型任务尤为重要。我们的方法作为一种通用的即插即用加速器，在Stable Diffusion v3.5（SD3.5）上实现了高达3倍的加速，在Lumina上实现了4倍加速。最值得注意的是，MAMBO-G将拥有140亿参数的Wan2.1视频模型的生成速度提升了2倍，同时保持了视觉保真度，为高效的大规模视频合成提供了一个实用的解决方案。我们的实现遵循主流开源扩散框架，并能与现有流程即插即用。