CoSIGN: Few-Step Guidance of ConSIstency Model to Solve General INverse Problems

• URL: http://arxiv.org/abs/2407.12676v1
• Date: Wed, 17 Jul 2024 15:57:50 GMT
• Title: CoSIGN: Few-Step Guidance of ConSIstency Model to Solve General INverse Problems
• Authors: Jiankun Zhao, Bowen Song, Liyue Shen,
• Abstract summary: We propose to push the boundary of inference steps to 1-2 NFEs while still maintaining high reconstruction quality. Our method achieves new state-of-the-art in diffusion-based inverse problem solving.
• Score: 3.3969056208620128
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Diffusion models have been demonstrated as strong priors for solving general inverse problems. Most existing Diffusion model-based Inverse Problem Solvers (DIS) employ a plug-and-play approach to guide the sampling trajectory with either projections or gradients. Though effective, these methods generally necessitate hundreds of sampling steps, posing a dilemma between inference time and reconstruction quality. In this work, we try to push the boundary of inference steps to 1-2 NFEs while still maintaining high reconstruction quality. To achieve this, we propose to leverage a pretrained distillation of diffusion model, namely consistency model, as the data prior. The key to achieving few-step guidance is to enforce two types of constraints during the sampling process of the consistency model: soft measurement constraint with ControlNet and hard measurement constraint via optimization. Supporting both single-step reconstruction and multistep refinement, the proposed framework further provides a way to trade image quality with additional computational cost. Within comparable NFEs, our method achieves new state-of-the-art in diffusion-based inverse problem solving, showcasing the significant potential of employing prior-based inverse problem solvers for real-world applications. Code is available at: https://github.com/BioMed-AI-Lab-U-Michgan/cosign.

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