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We introduce a novel sampler called the energy based diffusion generator forgenerating samples from arbitrary target distributions. The sampling modelemploys a structure similar to a variational autoencoder, utilizing a decoderto transform latent variables from a simple distribution into random variablesapproximating the target distribution, and we design an encoder based on thediffusion model. Leveraging the powerful modeling capacity of the diffusionmodel for complex distributions, we can obtain an accurate variational estimateof the Kullback-Leibler divergence between the distributions of the generatedsamples and the target. Moreover, we propose a decoder based on generalizedHamiltonian dynamics to further enhance sampling performance. Through empiricalevaluation, we demonstrate the effectiveness of our method across variouscomplex distribution functions, showcasing its superiority compared to existingmethods.

Energy based diffusion generator for efficient sampling of Boltzmann distributions