Papers › Analyzing and Improving the Training Dynamics of Diffusion Models

Analyzing and Improving the Training Dynamics of Diffusion Models

5 Dec 2023CVPR 2024 1arXiv:2312.02696archive 2025-07-28

Tero Karras, Miika Aittala, Jaakko Lehtinen, Janne Hellsten, Timo Aila, Samuli Laine

Diffusion models currently dominate the field of data-driven image synthesis with their unparalleled scaling to large datasets. In this paper, we identify and rectify several causes for uneven and ineffective training in the popular ADM diffusion model architecture, without altering its high-level structure. Observing uncontrolled magnitude changes and imbalances in both the network activations and weights over the course of training, we redesign the network layers to preserve activation, weight, and update magnitudes on expectation. We find that systematic application of this philosophy eliminates the observed drifts and imbalances, resulting in considerably better networks at equal computational complexity. Our modifications improve the previous record FID of 2.41 in ImageNet-512 synthesis to 1.81, achieved using fast deterministic sampling. As an independent contribution, we present a method for setting the exponential moving average (EMA) parameters post-hoc, i.e., after completing the training run. This allows precise tuning of EMA length without the cost of performing several training runs, and reveals its surprising interactions with network architecture, training time, and guidance.

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Code

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nvlabs/edm2 officialmentioned in papermentioned on GitHubpytorchNOASSERTION report
FutureXiang/edm2 mentioned on GitHubpytorch report
YichengDWu/tinyedm mentioned on GitHubpytorch report
dopplerchase/cira-diff mentioned on GitHubpytorch report
lucidrains/ema-pytorch mentioned on GitHubpytorch report
mmathew23/improved_edm mentioned on GitHubpytorchMIT report

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Code Syntology ran Syntology

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1ran · honoured contract
6ran · violated contract
1ran · our draft was wrong
10unverified

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Tasks

Image GenerationPhilosophy

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Image Generation ImageNet 512x512 EDM2-XXL FID 1.81 #20 of 52 Archive leaderboard report
Image Generation ImageNet 512x512 EDM2-XXL NFE 126 #20 of 52 Archive leaderboard report
Image Generation ImageNet 512x512 EDM2-XL FID 1.85 #21 of 52 Archive leaderboard report
Image Generation ImageNet 512x512 EDM2-XL NFE 126 #21 of 52 Archive leaderboard report
Image Generation ImageNet 512x512 EDM2-L FID 1.88 #22 of 52 Archive leaderboard report
Image Generation ImageNet 512x512 EDM2-L NFE 126 #22 of 52 Archive leaderboard report
Image Generation ImageNet 512x512 EDM2-M FID 2.01 #27 of 52 Archive leaderboard report
Image Generation ImageNet 512x512 EDM2-M NFE 126 #27 of 52 Archive leaderboard report
Image Generation ImageNet 512x512 EDM2-S FID 2.23 #31 of 52 Archive leaderboard report
Image Generation ImageNet 512x512 EDM2-S NFE 126 #31 of 52 Archive leaderboard report
Image Generation ImageNet 512x512 EDM2-XS FID 2.91 #39 of 52 Archive leaderboard report
Image Generation ImageNet 512x512 EDM2-XS NFE 126 #39 of 52 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Diffusion

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