Faster Training of Diffusion Models and Improved Density Estimation via
  Parallel Score Matching
Etrit Haxholli Marco Lorenzi
In Diffusion Probabilistic Models (DPMs), the task of modeling the score
evolution via a single time-dependent neural network necessitates extended
training periods and may potentially impede modeling flexibility and capacity.
To counteract these challenges, we propose leveraging the independence of
learning tasks at different time points inherent to DPMs. More specifically, we
partition the learning task by utilizing independent networks, each dedicated
to learning the evolution of scores within a specific time sub-interval.
Further, inspired by residual flows, we extend this strategy to its logical
conclusion by employing separate networks to independently model the score at
each individual time point. As empirically demonstrated on synthetic and image
datasets, our approach not only significantly accelerates the training process
by introducing an additional layer of parallelization atop data
parallelization, but it also enhances density estimation performance when
compared to the conventional training methodology for DPMs.