Image Diffusion Preview with Consistency Solver
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.
6940ceae65f1e24a11780328
Fu-Yun Wang
claimed_verified
2025-12-16T09:47:36.161Z
63e9e92f20c109718713f5eb
/avatars/9ff312e854d803e1a2e9e685a21d12f8.svg
Fu-Yun Wang
wangfuyun
user
wangfuyun
6940ceae65f1e24a11780329
Hao Zhou
6940ceae65f1e24a1178032a
Liangzhe Yuan
6940ceae65f1e24a1178032b
Sanghyun Woo
6940ceae65f1e24a1178032c
Boqing Gong
6940ceae65f1e24a1178032d
Bohyung Han
6940ceae65f1e24a1178032e
Ming-Hsuan Yang
6940ceae65f1e24a1178032f
Han Zhang
6940ceae65f1e24a11780330
Yukun Zhu
6940ceae65f1e24a11780331
Ting Liu
6940ceae65f1e24a11780332
Long Zhao
Image Diffusion Preview with Consistency Solver
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.
Diffusion Preview uses ConsistencySolver, a high-order trainable solver, to improve quality and consistency in low-step image generation, enhancing interactive user experiences.
image diffusion models Diffusion Preview rapid low-step sampling general linear multistep methods ConsistencySolver Reinforcement Learning FID scores Multistep DPM-Solver preview-and-refine workflows