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arXiv · 2505.21114

Differentiable Solver Search for Fast Diffusion Sampling

Shuai Wang·Zexian Li·Qipeng zhang·Tianhui Song·Xubin Li·Tiezheng Ge·Bo Zheng·Limin Wang
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Abstract

Diffusion models have demonstrated remarkable generation quality but at the cost of numerous function evaluations. Recently, advanced ODE-based solvers have been developed to mitigate the substantial computational demands of reverse-diffusion solving under limited sampling steps. However, these solvers, heavily inspired by Adams-like multistep methods, rely solely on t-related Lagrange interpolation. We show that t-related Lagrange interpolation is suboptimal for diffusion model and reveal a compact search space comprised of time steps and solver coefficients. Building on our analysis, we propose a novel differentiable solver search algorithm to identify more optimal solver. Equipped with the searched solver, rectified-flow models, e.g., SiT-XL/2 and FlowDCN-XL/2, achieve FID scores of 2.40 and 2.35, respectively, on ImageNet256 with only 10 steps. Meanwhile, DDPM model, DiT-XL/2, reaches a FID score of 2.33 with only 10 steps. Notably, our searched solver outperforms traditional solvers by a significant margin. Moreover, our searched solver demonstrates generality across various model architectures, resolutions, and model sizes.

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Shuai Wang, Zexian Li, Qipeng zhang, Tianhui Song, Xubin Li, Tiezheng Ge, Bo Zheng, Limin Wang. 2025-05-27. Differentiable Solver Search for Fast Diffusion Sampling. https://arxiv.org/abs/2505.21114

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