Papers › Improved Techniques for Training Score-Based Generative Models

Improved Techniques for Training Score-Based Generative Models

16 Jun 2020NeurIPS 2020 12arXiv:2006.09011archive 2025-07-28

Yang Song, Stefano Ermon

Score-based generative models can produce high quality image samples comparable to GANs, without requiring adversarial optimization. However, existing training procedures are limited to images of low resolution (typically below 32x32), and can be unstable under some settings. We provide a new theoretical analysis of learning and sampling from score models in high dimensional spaces, explaining existing failure modes and motivating new solutions that generalize across datasets. To enhance stability, we also propose to maintain an exponential moving average of model weights. With these improvements, we can effortlessly scale score-based generative models to images with unprecedented resolutions ranging from 64x64 to 256x256. Our score-based models can generate high-fidelity samples that rival best-in-class GANs on various image datasets, including CelebA, FFHQ, and multiple LSUN categories.

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ermongroup/ncsnv2 officialmentioned on GitHubpytorch report
AlexiaJM/AdversarialConsistentScoreMatching mentioned on GitHubpytorchMIT report
SamArgt/AudioSourceSep mentioned on GitHubtf report
ermongroup/ncsn mentioned on GitHubpytorch report
henryaddison/score_sde_pytorch mentioned on GitHubjaxApache-2.0 report
jmyoon1/adp mentioned on GitHubpytorch report
tpresser570/Lambert-Diffusion mentioned on GitHubpytorch report
yang-song/score_sde mentioned on GitHubpytorch report
yang-song/score_sde_pytorch mentioned on GitHubpytorch report

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2ran · our draft was wrong
1unverified

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Tasks

Image Generation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Image Generation CIFAR-10 NCSNv2 FID 10.87 #46 of 78 Archive leaderboard report

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