Denoising MCMC for Accelerating Diffusion-Based Generative Models

Beomsu Kim, Jong Chul Ye
Proceedings of the 40th International Conference on Machine Learning, PMLR 202:16955-16977, 2023.

Abstract

The sampling process of diffusion models can be interpreted as solving the reverse stochastic differential equation (SDE) or the ordinary differential equation (ODE) of the diffusion process, which often requires up to thousands of discretization steps to generate a single image. This has sparked a great interest in developing efficient integration techniques for reverse-S/ODEs. Here, we propose an orthogonal approach to accelerating score-based sampling: Denoising MCMC (DMCMC). DMCMC first uses MCMC to produce initialization points for reverse-S/ODE in the product space of data and diffusion time. Then, a reverse-S/ODE integrator is used to denoise the initialization points. Since MCMC traverses close to the data manifold, the cost of producing a clean sample for DMCMC is much less than that of producing a clean sample from noise. Denoising Langevin Gibbs, an instance of DMCMC, successfully accelerates all six reverse-S/ODE integrators considered in this work, and achieves state-of-the-art results: in the limited number of score function evaluation (NFE) setting on CIFAR10, we have $3.25$ FID with $\approx 10$ NFE and $2.49$ FID with $\approx 16$ NFE. On CelebA-HQ-256, we have $6.99$ FID with $\approx 160$ NFE, which beats the current best record of Kim et al. (2022) among score-based models, $7.16$ FID with $4000$ NFE. Code: https://github.com/1202kbs/DMCMC

Cite this Paper

BibTeX
@InProceedings{pmlr-v202-kim23z, title = {Denoising {MCMC} for Accelerating Diffusion-Based Generative Models}, author = {Kim, Beomsu and Ye, Jong Chul}, booktitle = {Proceedings of the 40th International Conference on Machine Learning}, pages = {16955--16977}, year = {2023}, editor = {Krause, Andreas and Brunskill, Emma and Cho, Kyunghyun and Engelhardt, Barbara and Sabato, Sivan and Scarlett, Jonathan}, volume = {202}, series = {Proceedings of Machine Learning Research}, month = {23--29 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v202/kim23z/kim23z.pdf}, url = {https://proceedings.mlr.press/v202/kim23z.html}, abstract = {The sampling process of diffusion models can be interpreted as solving the reverse stochastic differential equation (SDE) or the ordinary differential equation (ODE) of the diffusion process, which often requires up to thousands of discretization steps to generate a single image. This has sparked a great interest in developing efficient integration techniques for reverse-S/ODEs. Here, we propose an orthogonal approach to accelerating score-based sampling: Denoising MCMC (DMCMC). DMCMC first uses MCMC to produce initialization points for reverse-S/ODE in the product space of data and diffusion time. Then, a reverse-S/ODE integrator is used to denoise the initialization points. Since MCMC traverses close to the data manifold, the cost of producing a clean sample for DMCMC is much less than that of producing a clean sample from noise. Denoising Langevin Gibbs, an instance of DMCMC, successfully accelerates all six reverse-S/ODE integrators considered in this work, and achieves state-of-the-art results: in the limited number of score function evaluation (NFE) setting on CIFAR10, we have $3.25$ FID with $\approx 10$ NFE and $2.49$ FID with $\approx 16$ NFE. On CelebA-HQ-256, we have $6.99$ FID with $\approx 160$ NFE, which beats the current best record of Kim et al. (2022) among score-based models, $7.16$ FID with $4000$ NFE. Code: https://github.com/1202kbs/DMCMC} }
Endnote
%0 Conference Paper %T Denoising MCMC for Accelerating Diffusion-Based Generative Models %A Beomsu Kim %A Jong Chul Ye %B Proceedings of the 40th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2023 %E Andreas Krause %E Emma Brunskill %E Kyunghyun Cho %E Barbara Engelhardt %E Sivan Sabato %E Jonathan Scarlett %F pmlr-v202-kim23z %I PMLR %P 16955--16977 %U https://proceedings.mlr.press/v202/kim23z.html %V 202 %X The sampling process of diffusion models can be interpreted as solving the reverse stochastic differential equation (SDE) or the ordinary differential equation (ODE) of the diffusion process, which often requires up to thousands of discretization steps to generate a single image. This has sparked a great interest in developing efficient integration techniques for reverse-S/ODEs. Here, we propose an orthogonal approach to accelerating score-based sampling: Denoising MCMC (DMCMC). DMCMC first uses MCMC to produce initialization points for reverse-S/ODE in the product space of data and diffusion time. Then, a reverse-S/ODE integrator is used to denoise the initialization points. Since MCMC traverses close to the data manifold, the cost of producing a clean sample for DMCMC is much less than that of producing a clean sample from noise. Denoising Langevin Gibbs, an instance of DMCMC, successfully accelerates all six reverse-S/ODE integrators considered in this work, and achieves state-of-the-art results: in the limited number of score function evaluation (NFE) setting on CIFAR10, we have $3.25$ FID with $\approx 10$ NFE and $2.49$ FID with $\approx 16$ NFE. On CelebA-HQ-256, we have $6.99$ FID with $\approx 160$ NFE, which beats the current best record of Kim et al. (2022) among score-based models, $7.16$ FID with $4000$ NFE. Code: https://github.com/1202kbs/DMCMC
APA
Kim, B. & Ye, J.C.. (2023). Denoising MCMC for Accelerating Diffusion-Based Generative Models. Proceedings of the 40th International Conference on Machine Learning, in Proceedings of Machine Learning Research 202:16955-16977 Available from https://proceedings.mlr.press/v202/kim23z.html.

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