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

• URL: http://arxiv.org/abs/2608.17695v1
• Date: Tue, 18 Aug 2026 12:12:21 GMT
• Title: Magnitude-Direction Decoupling for Fast Video Generation with Flow Matching Models
• Abstract summary: Flow matching models for video generation suffer from high computational overhead due to iterative denoising.<n>We propose the Magnitude-Direction Decoupling (MDD) method, which adaptively employs a direction-calibrated lightweight model as a substitute.<n> Experiments show that MDD outperforms existing acceleration methods, delivering promising speedups.
• Score: 20.535268280651735
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Dual-Rate Diffusion: Accelerating diffusion models with an interleaved heavy-light network [33.60166262384809]
  Dual-Rate Diffusion is a method to accelerate sampling by interleaving the execution of a heavy high-capacity context encoder and a light efficient denoising model.<n>On ImageNet benchmarks, Dual-Rate Diffusion matches the performance of standard baselines while reducing computational cost by a factor of $2$-$4$.
  arXiv  Detail & Related papers  (2026-05-18T10:30:02Z)
• Noise Conditional Variational Score Distillation [60.38982038894823]
  Noise Conditional Variational Score Distillation (NCVSD) is a novel method for distilling pretrained diffusion models into generative denoisers.<n>By integrating this insight into the Variational Score Distillation framework, we enable scalable learning of generative denoisers.
  arXiv  Detail & Related papers  (2025-06-11T06:01:39Z)
• AudioTurbo: Fast Text-to-Audio Generation with Rectified Diffusion [23.250409921931492]
  Rectified flow enhances inference speed by learning straight-line ordinary differential equation paths.<n>This approach requires training a flow-matching model from scratch and tends to perform suboptimally, or even poorly, at low step counts.<n>We propose AudioTurbo, which learns first-order ODE paths from deterministic noise sample pairs generated by a pre-trained TTA model.
  arXiv  Detail & Related papers  (2025-05-28T08:33:58Z)
• On Equivariance and Fast Sampling in Video Diffusion Models Trained with Warped Noise [27.524057973734145]
  We show that warped noise can be trained to be equivariant to spatial transformations of the input noise.<n>This enables motion in the input noise to align naturally with motion in the generated video.<n>A further advantage is sampling efficiency: EquiVDM achieves comparable or superior quality in far fewer sampling steps.
  arXiv  Detail & Related papers  (2025-04-14T01:26:29Z)
• AB-Cache: Training-Free Acceleration of Diffusion Models via Adams-Bashforth Cached Feature Reuse [19.13826316844611]
  Diffusion models have demonstrated remarkable success in generative tasks, yet their iterative denoising process results in slow inference.<n>We provide a theoretical understanding by analyzing the denoising process through the second-order Adams-Bashforth method.<n>We propose a novel caching-based acceleration approach for diffusion models, instead of directly reusing cached results.
  arXiv  Detail & Related papers  (2025-04-13T08:29:58Z)
• One-Step Diffusion Model for Image Motion-Deblurring [85.76149042561507]
  We propose a one-step diffusion model for deblurring (OSDD), a novel framework that reduces the denoising process to a single step.<n>To tackle fidelity loss in diffusion models, we introduce an enhanced variational autoencoder (eVAE), which improves structural restoration.<n>Our method achieves strong performance on both full and no-reference metrics.
  arXiv  Detail & Related papers  (2025-03-09T09:39:57Z)
• Efficient Diffusion as Low Light Enhancer [63.789138528062225]
  Reflectance-Aware Trajectory Refinement (RATR) is a simple yet effective module to refine the teacher trajectory using the reflectance component of images. textbfReflectance-aware textbfDiffusion with textbfDistilled textbfTrajectory (textbfReDDiT) is an efficient and flexible distillation framework tailored for Low-Light Image Enhancement (LLIE)
  arXiv  Detail & Related papers  (2024-10-16T08:07:18Z)
• A-SDM: Accelerating Stable Diffusion through Redundancy Removal and Performance Optimization [54.113083217869516]
  In this work, we first explore the computational redundancy part of the network. We then prune the redundancy blocks of the model and maintain the network performance. Thirdly, we propose a global-regional interactive (GRI) attention to speed up the computationally intensive attention part.
  arXiv  Detail & Related papers  (2023-12-24T15:37:47Z)
• Towards More Accurate Diffusion Model Acceleration with A Timestep Tuner [112.99126045081046]
  A diffusion model, which is formulated to produce an image using thousands of denoising steps, usually suffers from a slow inference speed.<n>We propose a textbftimestep tuner that helps find a more accurate integral direction for a particular interval at the minimum cost.<n>Experiments show that our plug-in design can be trained efficiently and boost the inference performance of various state-of-the-art acceleration methods.
  arXiv  Detail & Related papers  (2023-10-14T02:19:07Z)
• Accelerating Diffusion Models via Early Stop of the Diffusion Process [114.48426684994179]
  Denoising Diffusion Probabilistic Models (DDPMs) have achieved impressive performance on various generation tasks. In practice DDPMs often need hundreds even thousands of denoising steps to obtain a high-quality sample. We propose a principled acceleration strategy, referred to as Early-Stopped DDPM (ES-DDPM), for DDPMs.
  arXiv  Detail & Related papers  (2022-05-25T06:40:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.