Papers › Analyzing and Improving the Training Dynamics of Diffusion Models
Analyzing and Improving the Training Dynamics of Diffusion Models
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
Syntology Ran 8 of 18 code samples harvested from 3 repositories linked to this paper; 10 have no recorded run. Of those that ran: 1 ran · honoured contract; 6 ran · violated contract; 1 ran · our draft was wrong.
By repository: community (archive-listed): 13 samples from 3 repositories, 3 ran; 5 identical to code first harvested elsewhere. The run record, sample by sample. “Ran” means executed on a synthesized input, not that the code is correct or reproduces the paper.
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
18 samples harvested; 8 ran; 1 honoured the contract we drafted; 10 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
Licence: 6 of the 18 samples are pointer only, meaning Syntology does not serve that copy's text. This page shows no code text for any sample; each one links to its file in the repository.
Harvested from 3 repositories linked to this paper, official or community; each sample names its own and says which. Some samples are identical code Syntology first harvested from another repository; for those, this paper's copy is not located and its licence is not recorded. “Ran” means the sample executed on a synthesized input. It does not mean the output is correct, and nothing here reproduces the paper's results. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 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
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