  Denoising diffusion bridge models (DDBMs) are a powerful variant of diffusion
models for interpolating between two arbitrary paired distributions given as
endpoints. Despite their promising performance in tasks like image translation,
DDBMs require a computationally intensive sampling process that involves the
simulation of a (stochastic) differential equation through hundreds of network
evaluations. In this work, we present diffusion bridge implicit models (DBIMs)
for accelerated sampling of diffusion bridges without extra training. We
generalize DDBMs via a class of non-Markovian diffusion bridges defined on the
discretized timesteps concerning sampling, which share the same training
objective as DDBMs. These generalized diffusion bridges give rise to generative
processes ranging from stochastic to deterministic (i.e., an implicit
probabilistic model) while being up to 25$\times$ faster than the vanilla
sampler of DDBMs. Moreover, the deterministic sampling procedure yielded by
DBIMs enables faithful encoding and reconstruction by a booting noise used in
the initial sampling step, and allows us to perform semantically meaningful
interpolation in image translation tasks by regarding the booting noise as the
latent variable.
