  Energy-Based Models (EBMs) offer a versatile framework for modeling complex
data distributions. However, training and sampling from EBMs continue to pose
significant challenges. The widely-used Denoising Score Matching (DSM) method
for scalable EBM training suffers from inconsistency issues, causing the energy
model to learn a `noisy' data distribution. In this work, we propose an
efficient sampling framework: (pseudo)-Gibbs sampling with moment matching,
which enables effective sampling from the underlying clean model when given a
`noisy' model that has been well-trained via DSM. We explore the benefits of
our approach compared to related methods and demonstrate how to scale the
method to high-dimensional datasets.
