RBF-Solver: A Multistep Sampler for Diffusion Probabilistic Models via Radial Basis Functions

• URL: http://arxiv.org/abs/2603.13330v1
• Date: Thu, 05 Mar 2026 06:19:44 GMT
• Title: RBF-Solver: A Multistep Sampler for Diffusion Probabilistic Models via Radial Basis Functions
• Abstract summary: Polynomial-based multistep samplers mitigate this cost by accelerating the sampling trajectory according to a predefined scheme.<n>We propose RBF-r, a multistep diffusion sampler that interpolates model evaluations with radial basis functions (RBFs)<n>By leveraging learnable shape parameters in RBFs, RBF-r explicitly follows optimal sampling trajectories.
• Score: 1.7498738677484031
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Diffusion probabilistic models (DPMs) are widely adopted for their outstanding generative fidelity, yet their sampling is computationally demanding. Polynomial-based multistep samplers mitigate this cost by accelerating inference; however, despite their theoretical accuracy guarantees, they generate the sampling trajectory according to a predefined scheme, providing no flexibility for further optimization. To address this limitation, we propose RBF-Solver, a multistep diffusion sampler that interpolates model evaluations with Gaussian radial basis functions (RBFs). By leveraging learnable shape parameters in Gaussian RBFs, RBF-Solver explicitly follows optimal sampling trajectories. At first order, it reduces to the Euler method (DDIM). At second order or higher, as the shape parameters approach infinity, RBF-Solver converges to the Adams method, ensuring its compatibility with existing samplers. Owing to the locality of Gaussian RBFs, RBF-Solver maintains high image fidelity even at fourth order or higher, where previous samplers deteriorate. For unconditional generation, RBF-Solver consistently outperforms polynomial-based samplers in the high-NFE regime (NFE >= 15). On CIFAR-10 with the Score-SDE model, it achieves an FID of 2.87 with 15 function evaluations and further improves to 2.48 with 40 function evaluations. For conditional ImageNet 256 x 256 generation with the Guided Diffusion model at a guidance scale 8.0, substantial gains are achieved in the low-NFE range (5-10), yielding a 16.12-33.73% reduction in FID relative to polynomial-based samplers.

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