PreprintarXiv licencearXiv
Accurate Sampling from Diffusion Models
Abstract
A new proposal called DM-SMC (Diffusion Model - Sequential Monte Carlo) is investigated, which samples ensembles defined in terms of an action, using diffusion models trained on samples from the ensemble. The SMC setup allows for accurate sampling in spite of an approximate diffusion model and the finite stepsize used in the numerical solution of the stochastic process. Improved update strategies are also investigated. Results are presented for a Z2 symmetric scalar field theory in 2 dimensions near its 2nd order phase transition.
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BibTeX
@misc{sexty2026accurate,
title = {{Accurate Sampling from Diffusion Models}},
author = {Dénes Sexty},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.29902},
eprint = {2609.29902},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.29902},
}
RIS
TY - UNPB
TI - Accurate Sampling from Diffusion Models
AU - Dénes Sexty
PY - 2026
DA - 2026/09/24
PB - arXiv
DO - 10.48550/arxiv.2609.29902
UR - https://arxiv.org/abs/2609.29902
AB - A new proposal called DM-SMC (Diffusion Model - Sequential Monte Carlo) is investigated, which samples ensembles defined in terms of an action, using diffusion models trained on samples from the ensemble. The SMC setup allows for accurate sampling in spite of an approximate diffusion model and the finite stepsize used in the numerical solution of the stochastic process. Improved update strategies are also investigated. Results are presented for a Z_2 symmetric scalar field theory in 2 dimensions near its 2nd order phase transition.
ER -