LEAP: Accelerating ODE-based Diffusion Sampling with Optimal Linear Leaps

Key points

• Diffusion models primarily rely on general-purpose ODE-based numerical methods for training-free accelerated sampling, but performance degrades with coarse time discretization.1
• Built upon low-dimensionality and cross-trajectory consistency, LEAP formulates a supervised least-squares optimization problem within the local trajectory subspace, achieving a precise approximation of future states without model evaluations.1
• Extensive experiments demonstrate that LEAP outperforms previous methods across diverse modalities, including text-to-image (Stable Diffusion 3.5, FLUX.1-dev) and image-to-3D (TRELLIS.2) generation.1
• Furthermore, LEAP can be seamlessly integrated into existing solver-based methods, offering a plug-and-play enhancement for modern diffusion models.1

Sources (1)

• [1]LEAP: Accelerating ODE-based Diffusion Sampling with Optimal Linear Leaps
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 07:19 AM

  In this paper, we propose optimal linear leaps (LEAP), a specialized method grounded in theoretical and empirical investigations of sampling trajectory geometry.

  Diffusion models primarily rely on general-purpose ODE-based numerical methods for training-free accelerated sampling, but performance degrades with coarse time discretization.

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