Magnitude-Direction Decoupling for Fast Video Generation with Flow Matching Models

2026-08-18

AI Summary

为解决视频生成中流匹配模型的高计算开销问题，提出了一种幅度-方向解耦方法（MDD），通过使用轻量级模型和缓存来加速推理过程。

Abstract

Flow matching models for video generation achieve impressive performance but suffer from high computational overhead due to iterative denoising. In fact, the original model is not necessary for all denoising steps, allowing some steps to use lightweight alternatives for faster sampling. However, directly using caching or lightweight models can deviate from the original denoising trajectory, resulting in suboptimal performance. Through empirical analysis, we find that lightweight models can robustly capture the magnitude components of the original model's output, while caching provides reliable directional guidance. Building on this insight, we propose the Magnitude-Direction Decoupling (MDD) method, which adaptively employs a direction-calibrated lightweight model as a substitute for the original model to accelerate inference and effectively correct deviations in the denoising trajectory. Moreover, MDD further reduces inference costs by reusing magnitude information under classifier-free guidance (CFG). As a result, MDD offers a more reliable and lightweight solution to accelerate sampling. Experiments show that MDD outperforms existing acceleration methods, delivering promising speedups (e.g., up to 2.95x on Wan2.1) while preserving high visual fidelity and content richness.

Problem

• video generation
• flow matching models
• computational overhead
• iterative denoising
• lightweight models

Innovation

• Magnitude-Direction Decoupling
• Flow Matching Models
• Lightweight Model
• Denoising Trajectory
• Classifier-Free Guidance