  Simulation-based inference (SBI) is constantly in search of more expressive
algorithms for accurately inferring the parameters of complex models from noisy
data. We present consistency models for neural posterior estimation (CMPE), a
new free-form conditional sampler for scalable, fast, and amortized SBI with
generative neural networks. CMPE combines the advantages of normalizing flows
and flow matching methods into a single generative architecture: It essentially
distills a continuous probability flow and enables rapid few-shot inference
with an unconstrained architecture that can be tailored to the structure of the
estimation problem. Our empirical evaluation demonstrates that CMPE not only
outperforms current state-of-the-art algorithms on three hard low-dimensional
problems but also achieves competitive performance in a high-dimensional
Bayesian denoising experiment and in estimating a computationally demanding
multi-scale model of tumor spheroid growth.
