Papers › Improved Denoising Diffusion Probabilistic Models
Improved Denoising Diffusion Probabilistic Models
Alex Nichol, Prafulla Dhariwal
Denoising diffusion probabilistic models (DDPM) are a class of generative models which have recently been shown to produce excellent samples. We show that with a few simple modifications, DDPMs can also achieve competitive log-likelihoods while maintaining high sample quality. Additionally, we find that learning variances of the reverse diffusion process allows sampling with an order of magnitude fewer forward passes with a negligible difference in sample quality, which is important for the practical deployment of these models. We additionally use precision and recall to compare how well DDPMs and GANs cover the target distribution. Finally, we show that the sample quality and likelihood of these models scale smoothly with model capacity and training compute, making them easily scalable. We release our code at https://github.com/openai/improved-diffusion
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Code
Syntology Ran 9 of 11 code samples harvested from 4 repositories linked to this paper; 2 have no recorded run. Of those that ran: 4 ran · honoured contract; 4 ran · our draft was wrong; 1 ran · fixture could not drive it.
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Code Syntology ran Syntology
11 samples harvested; 9 ran; 4 honoured the contract we drafted; 2 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.
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Harvested from 4 repositories linked to this paper, official or community; each sample names its own and says which. “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 256x256 | Improved DDPM | FID | 12.3 | #94 of 94 | Archive leaderboard | report |
| Image Generation | ImageNet 64x64 | Improved DDPM | Bits per dim | 3.53 | #21 of 65 | Archive leaderboard | report |
| Image Generation | ImageNet 64x64 | Improved DDPM | FID | 2.92 | #21 of 65 | 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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