[83] Max Revay, Ruigang Wang, and Ian R Manchester. Lipschitz bounded equilibrium networks. arXiv preprint arXiv:2010.01732, 2020. 18
[84] Adam Roberts, Colin Raffel, Katherine Lee, Michael Matena, Noam Shazeer, Peter J Liu, Sharan Narang, Wei Li, and Yanqi Zhou. Exploring the limits of transfer learning with a unified text-to-text transformer. 2019. 17
[85] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10684-10695, 2022. 1, 18
[86] Chitwan Sahara, William Chan, Huiwen Chang, Chris Lee, Jonathan Ho, Tim Salimans, David Fleet, and Mohammad Norouzi. Palette: Image-to-image diffusion models. In ACM SIGGRAPH 2022 Conference Proceedings, pages 1-10, 2022. 1
[87] Chitwan Sahara, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L Denton, Kamyar Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, et al. Photorealistic text-to-image diffusion models with deep language understanding. Neural Information Processing Systems (NeurIPS), 2022. 1
[88] Tim Salimans and Jonathan Ho. Progressive distillation for fast sampling of diffusion models. arXiv preprint arXiv:2202.00512, 2022. 1, 2, 6, 7, 8, 9, 10
[89] Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen. Improved techniques for training gans. Advances in neural information processing systems, 29, 2016. 6
[90] Robin San-Roman, Eliya Nachmani, and Lior Wolf. Noise estimation for generative diffusion models. arXiv preprint arXiv:2104.02600, 2021. 9
[91] Axel Sauer, Katja Schwarz, and Andreas Geiger. Stylegan-xl: Scaling stylegan to large diverse datasets. In ACM SIGGRAPH 2022 conference proceedings, pages 1-10, 2022. 8
[92] Uriel Singer, Adam Polyak, Thomas Hayes, Xi Yin, Jie An, Songyang Zhang, Qiyuan Hu, Harry Yang, Oron Ashual, Oran Gafni, et al. Make-a-video: Text-to-video generation without text-video data. arXiv preprint arXiv:2209.14792, 2022. 1
[93] Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli. Deep unsupervised learning using nonequilibrium thermodynamics. In International Conference on Machine Learning (ICML), 2015. 1, 3
[94] Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. In International Conference on Learning Representations (ICLR), 2021. 3, 8, 9
[95] Yang Song and Stefano Ermon. Generative modeling by estimating gradients of the data distribution. Neural Information Processing Systems (NeurIPS), 2019. 1, 3
[96] Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole. Score-based generative modeling through stochastic differential equations. In International Conference on Learning Representations (ICLR), 2021. 1, 3, 8, 9
[97] Yang Song, Prafulla Dhariwal, Mark Chen, and Ilya Sutskever. Consistency models. arXiv preprint arXiv:2303.01469, 2023. 1, 7, 8, 9
[98] Russell Tsuchida and Cheng Soon Ong. Deep equilibrium models as estimators for continuous latent variables. In International Conference on Artificial Intelligence and Statistics, pages 1646-1671. PMLR, 2023. 18
[99] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. Neural Information Processing Systems (NeurIPS), 2017. 3, 4, 17, 18
[100] Shuai Wang, Yao Teng, and Limin Wang. Deep equilibrium object detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 6296-6306, 2023. 18