Consistency Models for Scalable and Fast Simulation-Based Inference

We present consistency models for posterior estimation (CMPE), a new free-form generative model family grounded in diffusion model theory. CMPE enables rapid few-shot inference with an unconstrained architecture that can be flexibly tailored to the model's symmetry. We provide hyperparameters that support consistency training over a wide range of different dimensions, including low-dimensional ones which are important in simulaton-based inference but were previously difficult to tackle.