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References

  1. Anderson, B. D. O. Reverse-time diffusion equation models. Stochastic Processes and their Applications 12, 313-326 (1982).
  2. Bishop, C. M. Pattern Recognition and Machine Learning. (Springer-Verlag, 2006).
  3. Chen, T. On the Importance of Noise Scheduling for Diffusion Models. Preprint (2023).
  4. Chen, T. Q., Rubanova, Y., Bettencourt, J. & Duvenaud, D. K. Neural Ordinary Differential Equations. in Advances in Neural Information Processing Systems 31 (eds. Bengio, S. et al.) 6571-6583 (Curran Associates, Inc., 2018).
  5. Dhariwal, P. & Nichol, A. Diffusion Models Beat GANs on Image Synthesis. in Advances in Neural Information Processing Systems vol. 34 8780-8794 (Curran Associates, Inc., 2021).
  6. Hang, T. et al. Efficient Diffusion Training via Min-SNR Weighting Strategy. in Proceedings of the IEEE/CVF International Conference on Computer Vision 7441-7451 (2023).
  7. Ho, J., Jain, A. & Abbeel, P. Denoising Diffusion Probabilistic Models. in Advances in Neural Information Processing Systems vol. 33 6840-6851 (2020).
  8. Ho, J. & Salimans, T. Classifier-Free Diffusion Guidance. Preprint at https://doi.org/10.48550/arXiv.2207.12598 (2022).
  9. Hoogeboom, E., Heek, J. & Salimans, T. simple diffusion: End-to-end diffusion for high resolution images. in Proceedings of the 40th International Conference on Machine Learning 13213-13232 (PMLR, 2023).
  10. Johnson, N. L., Kotz, S. & Balakrishnan, N. Continuous Univariate Distributions, Vol. 1. (Wiley-Interscience, 1994).
  11. Karras, T., Aittala, M., Aila, T. & Laine, S. Elucidating the Design Space of Diffusion-Based Generative Models. in Advances in Neural Information Processing Systems vol. 35 26565-26577 (2022).
  12. Kingma, D. P. & Welling, M. Auto-Encoding Variational Bayes. in International Conference on Learning Representations (2014).
  13. Kingma, D., Salimans, T., Poole, B. & Ho, J. Variational Diffusion Models. in Advances in Neural Information Processing Systems vol. 34 21696-21707 (Curran Associates, Inc., 2021).
  14. Liu, L., Ren, Y., Lin, Z. & Zhao, Z. Pseudo Numerical Methods for Diffusion Models on Manifolds. in International Conference on Learning Representations (2022).
  15. Lu, C. et al. DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps. in Advances in Neural Information Processing Systems vol. 35 5775-5787 (2022).
  16. Lucic, M., Kurach, K., Michalski, M., Gelly, S. & Bousquet, O. Are GANs Created Equal? A Large-Scale Study. in Advances in Neural Information Processing Systems 31 (eds. Bengio, S. et al.) 700-709 (Curran Associates, Inc., 2018).
  17. Nichol, A. Q. & Dhariwal, P. Improved Denoising Diffusion Probabilistic Models. in Proceedings of the 38th International Conference on Machine Learning 8162-8171 (PMLR, 2021)
  18. Salimans, T. & Ho, J. Progressive Distillation for Fast Sampling of Diffusion Models. in International Conference on Learning Representations (2022).
  19. Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N. & Ganguli, S. Deep Unsupervised Learning using Nonequilibrium Thermodynamics. in Proceedings of the 32nd International Conference on Machine Learning 2256-2265 (PMLR, 2015).
  20. Song, Y. & Ermon, S. Generative Modeling by Estimating Gradients of the Data Distribution. in Advances in Neural Information Processing Systems 32 (eds. Wallach, H. et al.) 11895-11907 (Curran Associates, Inc., 2019).
  21. Song, J., Meng, C. & Ermon, S. Denoising Diffusion Implicit Models. in International Conference on Learning Representations (2021).
  22. Song, Y. et al. Score-Based Generative Modeling through Stochastic Differential Equations. in International Conference on Learning Representations (2021).
  23. Song, Y., Dhariwal, P., Chen, M. & Sutskever, I. Consistency Models. in Proceedings of the 40th International Conference on Machine Learning 32211-32252 (PMLR, 2023).
  24. Song, Y. & Dhariwal, P. Improved Techniques for Training Consistency Models. Preprint (2023).
  25. Vaswani, A. et al. Attention is All you Need. in Advances in Neural Information Processing Systems 30 (eds. Guyon, I. et al.) 5998-6008 (Curran Associates, Inc., 2017).
  26. Vincent, P. A Connection Between Score Matching and Denoising Autoencoders. Neural Computation 23, 1661-1674 (2011).
  27. Zhang, Q. & Chen, Y. Fast Sampling of Diffusion Models with Exponential Integrator. in International Conference on Learning Representations (2023).