Image Diffusion Preview with Consistency Solver

• URL: http://arxiv.org/abs/2512.13592v1
• Date: Mon, 15 Dec 2025 17:47:49 GMT
• Title: Image Diffusion Preview with Consistency Solver
• Abstract summary: We introduce Diffusion Preview, a novel paradigm employing rapid, low-step sampling to generate preliminary outputs for user evaluation.<n>Existing acceleration methods, including training-free solvers and post-training distillation, struggle to deliver high-quality previews.<n>We propose Consistencyr derived from general linear multistep methods, a lightweight, trainable high-order solver via Reinforcement Learning.
• Score: 86.95276270716477
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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