  We introduce ensembles within score-based sampling methods to develop
gradient-free approximate sampling techniques that leverage the collective
dynamics of particle ensembles to compute approximate reverse diffusion drifts.
We introduce the underlying methodology, emphasizing its relationship with
generative diffusion models and the previously introduced F\"ollmer sampler. We
demonstrate the efficacy of ensemble strategies through various examples,
ranging from low- to medium-dimensionality sampling problems, including
multi-modal and highly non-Gaussian probability distributions, and provide
comparisons to traditional methods like NUTS. Our findings highlight the
potential of ensemble strategies for modeling complex probability distributions
in situations where gradients are unavailable. Finally, we showcase its
application in the context of Bayesian inversion problems within the
geophysical sciences.
