The slow inference process of image diffusion models significantly degrades interactive user experiences. To address this, we introduce Diffusion Preview, a novel paradigm employing rapid, low-step sampling to generate preliminary outputs for user evaluation, deferring full-step refinement until the preview is deemed satisfactory. Existing acceleration methods, including training-free solvers and post-training distillation, struggle to deliver high-quality previews or ensure consistency between previews and final outputs. We propose ConsistencySolver derived from general linear multistep methods, a lightweight, trainable high-order solver optimized via Reinforcement Learning, that enhances preview quality and consistency. Experimental results demonstrate that ConsistencySolver significantly improves generation quality and consistency in low-step scenarios, making it ideal for efficient preview-and-refine workflows. Notably, it achieves FID scores on-par with Multistep DPM-Solver using 47% fewer steps, while outperforming distillation baselines. Furthermore, user studies indicate our approach reduces overall user interaction time by nearly 50% while maintaining generation quality. Code is available at https://github.com/G-U-N/consolver.
@article{arxiv.2512.13592,
title = {Image Diffusion Preview with Consistency Solver},
author = {Fu-Yun Wang and Hao Zhou and Liangzhe Yuan and Sanghyun Woo and Boqing Gong and Bohyung Han and Ming-Hsuan Yang and Han Zhang and Yukun Zhu and Ting Liu and Long Zhao},
journal= {arXiv preprint arXiv:2512.13592},
year = {2026}
}