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Consistency models, which were proposed to mitigate the high computational overhead during the sampling phase of diffusion models, facilitate single-step sampling while attaining state-of-the-art empirical performance.
Reverse-time diffusion equation models
Anderson, B. D. (1982) · 1982
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Time reversal of diffusions
Haussmann, U. G. and Pardoux, E. (1986) · 1986
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Generative modeling with denoising auto-encoders and Langevin sampling
Block, A., Mroueh, Y., and Rakhlin, A. (2020) · 2002
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Estimation of non-normalized statistical models by score matching
Hyvärinen, A. (2005) · 2005
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Diffwave: A versatile diffusion model for audio synthesis
Kong, Z., Ping, W., Huang, J., Zhao, K., and Catanzaro, B. (2020) · 2009
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Denoising diffusion implicit models
Song, J., Meng, C., and Ermon, S. (2020) · 2010
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A connection between score matching and denoising autoencoders
Vincent, P. (2011) · 2011
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Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., and Ganguli, S. (2015) · 2015
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Generative modeling by estimating gradients of the data distribution
Song, Y. and Ermon, S. (2019) · 2019
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Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P. (2020) · 2020
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Improved techniques for training score-based generative models
Song, Y. and Ermon, S. (2020) · 2020
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Diffusion Schrödinger bridge with applications to score-based generative modeling
De Bortoli, V., Thornton, J., Heng, J., and Doucet, A. (2021) · 2021
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Diffusion models beat gans on image synthesis
Dhariwal, P. and Nichol, A. (2021) · 2021
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Knowledge distillation in iterative generative models for improved sampling speed
Luhman, E. and Luhman, T. (2021) · 2021
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Improved denoising diffusion probabilistic models
Nichol, A. Q. and Dhariwal, P. (2021) · 2021
Cited alongside, same era.
Grad-tts: A diffusion probabilistic model for text-to-speech
Popov, V., Vovk, I., Gogoryan, V., Sadekova, T., and Kudinov, M. (2021) · 2021
Cited alongside, same era.
Score-based generative modeling through stochastic differential equations
Song, Y., Sohl-Dickstein, J., Kingma, D. P., Kumar, A., Ermon, S., and Poole, B. (2021) · 2021
Cited alongside, same era.
Convergence of denoising diffusion models under the manifold hypothesis
De Bortoli, V. (2022) · 2022
Cited alongside, same era.
Imagen video: High definition video generation with diffusion models
Ho, J., Chan, W., Saharia, C., Whang, J., Gao, R., Gritsenko, A., Kingma, D. P., Poole, B., Norouzi, M., Fleet, D. J., et al. (2022) · 2022
Cited alongside, same era.
Consistency trajectory models: Learning probability flow ode trajectory of diffusion
Kim, D., Lai, C.-H., Liao, W.-H., Murata, N., Takida, Y., Uesaka, T., He, Y., Mitsufuji, Y., and Ermon, S. (2023) · 2023
Later among the works it cites.
Convergence of score-based generative modeling for general data distributions
Lee, H., Lu, J., and Tan, Y. (2023) · 2023
Later among the works it cites.
Towards faster non-asymptotic convergence for diffusion-based generative models
Li, G., Wei, Y., Chen, Y., and Chi, Y. (2023) · 2023
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On distillation of guided diffusion models
Meng, C., Rombach, R., Gao, R., Kingma, D., Ermon, S., Ho, J., and Salimans, T. (2023) · 2023
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Improved techniques for training consistency models
Song, Y. and Dhariwal, P. (2023) · 2023
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Elucidating the design space of diffusion-based generative models
Karras, T., Aittala, M., Aila, T., and Laine, S. (2022) · 2022
Cited alongside, same era.
Let us build bridges: Understanding and extending diffusion generative models
Liu, X., Wu, L., Ye, M., and Liu, Q. (2022) · 2022
Cited alongside, same era.
Score-based generative models detect manifolds
Pidstrigach, J. (2022) · 2022
Cited alongside, same era.
Hierarchical text-conditional image generation with CLIP latents
Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., and Chen, M. (2022) · 2022
Cited alongside, same era.
High-resolution image synthesis with latent diffusion models
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B. (2022) · 2022
Cited alongside, same era.
Progressive distillation for fast sampling of diffusion models
Salimans, T. and Ho, J. (2022) · 2022
Cited alongside, same era.
Fast sampling of diffusion models with exponential integrator
Zhang, Q. and Chen, Y. (2022) · 2022
Cited alongside, same era.
Later among the works it cites.
Consistency models
Song, Y., Dhariwal, P., Chen, M., and Sutskever, I. (2023) · 2023
Later among the works it cites.
Accelerating diffusion sampling with classifier-based feature distillation
Sun, W., Chen, D., Wang, C., Ye, D., Feng, Y., and Chen, C. (2023) · 2023
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Diffusion probabilistic models
Tang, W. (2023) · 2023
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Videolcm: Video latent consistency model
Wang, X., Zhang, S., Zhang, H., Liu, Y., Zhang, Y., Gao, C., and Sang, N. (2023) · 2023
Later among the works it cites.
SA-Solver: Stochastic Adams solver for fast sampling of diffusion models
Xue, S., Yi, M., Luo, W., Zhang, S., Sun, J., Li, Z., and Ma, Z.-M. (2023) · 2023
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UniPC: A unified predictor-corrector framework for fast sampling of diffusion models
Zhao, W., Bai, L., Rao, Y., Zhou, J., and Lu, J. (2023) · 2023
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Accelerating convergence of score-based diffusion models, provably
Li, G., Huang, Y., Efimov, T., Wei, Y., Chi, Y., and Chen, Y. (2024) · 2024
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Contractive diffusion probabilistic models
Tang, W. and Zhao, H. (2024) · 2024
Closest in time.