PnP-CM: Consistency Models as Plug-and-Play Priors for Inverse Problems

Researchers introduce PnP-CM, a new method that reformulates consistency models as proximal operators within plug-and-play frameworks for solving inverse problems. The approach achieves high-quality image reconstructions with minimal neural function evaluations (4 NFEs), demonstrating practical efficiency gains over existing consistency model solvers and marking the first application of CMs to MRI data.

PnP-CM represents a meaningful advancement in computational efficiency for inverse problems—a fundamental challenge across imaging, reconstruction, and signal processing. The research reframes consistency models, which were designed to accelerate diffusion sampling, as proximal operators compatible with ADMM-based optimization. This conceptual bridge enables practitioners to leverage fast CM inference without task-specific retraining, addressing a critical bottleneck in prior CM-based approaches that required domain adaptation or slow convergence properties.

The significance lies in the framework's generality. By operating as a plug-and-play component, PnP-CM extends to both linear problems (denoising, super-resolution) and nonlinear scenarios (MRI reconstruction) without architectural modification. The ability to produce meaningful results in just 2 steps and high-quality outputs in 4 neural evaluations demonstrates practical applicability for resource-constrained environments, from edge computing to real-time medical imaging.

For applied AI practitioners and imaging researchers, this removes friction from deploying generative priors. Traditional diffusion-based inverse problem solvers require 50-1000+ function evaluations; reducing this to single digits while maintaining quality substantially expands deployment possibilities. The incorporation of noise perturbations and momentum updates targets the critical low-evaluation regime where most deployed systems operate.

Looking forward, the convergence of consistency models with classical optimization frameworks suggests broader integration of modern generative techniques into structured solvers. Validation on real MRI data opens medical imaging applications, while the plug-and-play design enables rapid adaptation to emerging domains without retraining.

• →PnP-CM achieves high-quality image reconstructions with only 4 neural function evaluations, drastically reducing computational requirements versus existing methods.
• →The method works as a plug-and-play component across linear and nonlinear inverse problems without requiring task-specific retraining.
• →First successful application of consistency models to MRI data demonstrates practical viability for medical imaging workflows.
• →ADMM-based framework with momentum updates and noise perturbations enables meaningful results in just 2 steps for resource-constrained scenarios.
• →Reinterpreting consistency models as proximal operators bridges generative AI with classical optimization theory, enabling practical hybrid approaches.