ParaLin++: Accelerating Parallel Diffusion Integrator via Intrinsic Partially Linear Structure

Jianrong Lu; Zhiyu Zhu; Hui Liu; Junhui Hou

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arXiv · 2610.30704

ParaLin++: Accelerating Parallel Diffusion Integrator via Intrinsic Partially Linear Structure

Jianrong Lu·Zhiyu Zhu·Hui Liu·Junhui Hou

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Abstract

Training-free few-step samplers are often described as nearly quality-lossless under conventional FID/CLIP-style evaluation. It remains unclear whether that conclusion transfers to more realistic and complex generation-quality benchmarks. We first systematize the quality metrics used to evaluate training-free sampling acceleration for diffusion and flow-matching models. We find that representative training-free step-shortening methods often remain nearly lossless under FID/CLIP-class metrics, yet can suffer substantial quality degradation when evaluated with semantic-composition, dense-instruction, and long-text-rendering benchmarks such as GenEval, DPG-Bench, and LongTextBench. Parallel sampling is a notable exception: by retaining the full-step discretization and removing only the solver's temporal dependency, it can preserve near-lossless quality while accelerating sampling. Building on this observation, we propose \textit{ParaLin++}, a step-parity parallel sampler that preserves the full-step discretization while accelerating the solver iterations. Experiments across Stable Diffusion-v2, LSUN Church, FLUX.1-dev, and Ming-Flash-Omni 2.0 show that ParaLin++ preserves the full-step Euler quality profile at $2.6\times$--$4.8\times$, $1.68\times$ on FLUX, and $1.21\times$ on Ming. Project page: https://paralinplus.github.io/.

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BibTeX RIS

Jianrong Lu, Zhiyu Zhu, Hui Liu, Junhui Hou. 2026-10-22. ParaLin++: Accelerating Parallel Diffusion Integrator via Intrinsic Partially Linear Structure. https://arxiv.org/abs/2610.30704

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