mlsanityData to 7 Oct 2026

Denoising Diffusion Implicit Models

arXiv 2010.02502 ↗First posted 6 Oct 2020cs.LG cs.CVcited by 9,655

On the map: Diffusion models → Diffusion foundations and sampling

Abstract (arXiv)

Denoising diffusion probabilistic models (DDPMs) have achieved high quality image generation without adversarial training, yet they require simulating a Markov chain for many steps to produce a sample. To accelerate sampling, we present denoising diffusion implicit models (DDIMs), a more efficient class of iterative implicit probabilistic models with the same training procedure as DDPMs. In DDPMs, the generative process is defined as the reverse of a Markovian diffusion process. We construct a class of non-Markovian diffusion processes that lead to the same training objective, but whose reverse process can be much faster to sample from. We empirically demonstrate that DDIMs can produce high quality samples 10 × to 50 × faster in terms of wall-clock time compared to DDPMs, allow us to trade off computation for sample quality, and can perform semantically meaningful image interpolation directly in the latent space.

Key facts. Cited by 9,655 papers in the map (data through 7 Oct 2026). Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models. Builds on 29 of its 48 references that are in the map. First posted on arXiv 6 Oct 2020.

Builds on

29 of 48 references.

Closest ancestors · 2

Same direction, newest first

  1. 2020Denoising Diffusion Probabilistic Models19 Jun
  2. 2020Improved Techniques for Training Score-Based Generative Models16 Jun

Other references · 27

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
  3. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 5,986
  4. 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,067
  5. 2013Auto-Encoding Variational Bayescited by 3,980
  6. 2018Neural Ordinary Differential Equationscited by 3,733
  7. 2019Analyzing and Improving the Image Quality of StyleGANcited by 3,580
  8. 2016Wide Residual Networkscited by 3,057
  9. 2018Large Scale GAN Training for High Fidelity Natural Image Synthesiscited by 2,609
  10. 2017Improved Training of Wasserstein GANscited by 2,223
  11. 2015Variational Inference with Normalizing Flowscited by 2,161
  12. 2016Density estimation using Real NVPcited by 2,029
15 more
  1. 2016WaveNet: A Generative Model for Raw Audiocited by 1,710
  2. 2016Pixel Recurrent Neural Networkscited by 814
  3. 2017Wasserstein GANcited by 635
  4. 2020WaveGrad: Estimating Gradients for Waveform Generationcited by 620
  5. 2018FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Modelscited by 584
  6. 2017GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibriumcited by 208
  7. 2019Seeing What a GAN Cannot Generatecited by 148
  8. 2016Learning in Implicit Generative Modelscited by 124
  9. 2020Adversarial score matching and improved sampling for image generationcited by 95
  10. 2018Bias and Generalization in Deep Generative Models: An Empirical Studycited by 83
  11. 2017Generalizing Hamiltonian Monte Carlo with Neural Networkscited by 52
  12. 2020Continuous-in-Depth Neural Networkscited by 40
  13. 2013Deep Generative Stochastic Networks Trainable by Backpropcited by 36
  14. 2017A-NICE-MC: Adversarial Training for MCMCcited by 36
  15. 2017Variational Walkback: Learning a Transition Operator as a Stochastic Recurrent Netcited by 30

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Citing papers per year: 2021: 61; 2022: 308; 2023: 1605; 2024: 2680; 2025: 2998; 2026: 2003.

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