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A Lagrange-Interpolated Sampler for Diffusion Probabilistic Models

algorithms-and-analysis-of-algorithms
artificial-intelligence
computer-vision
data-mining-and-machine-learning
optimization-theory-and-computation

Abstract

We propose the Lagrange-Interpolated Sampler (LIS), a novel high-order sampling algorithm for diffusion probabilistic models. Unlike existing sampler algorithms, LIS leverages high-order Lagrange polynomial interpolation to approximate the reverse-time score function across diffusion steps. Our method dynamically aggregates previous model outputs to construct a more accurate trajectory of denoising, thereby improving sample fidelity while maintaining computational efficiency. Empirical results demonstrate that LIS achieves competitive or superior performance in image generation tasks, especially under constrained inference steps, highlighting its potential as a training-free, plug-and-play component in diffusion pipelines.
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