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Recently, diffusion distillation methods have compressed thousand-step teacher diffusion models into one-step student generators while preserving sample quality.
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Y. Song, J. Sohl-Dickstein, D. P. Kingma, A. Kumar, S. Ermon, and B. Poole · 2020
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Diffusion schrödinger bridge with applications to score-based generative modeling
V. De Bortoli, J. Thornton, J. Heng, and A. Doucet · 2021
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Improved denoising diffusion probabilistic models
A. Q. Nichol and P. Dhariwal · 2021
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Score-based generative modeling through stochastic differential equations
Y. Song, J. Sohl-Dickstein, D. P. Kingma, A. Kumar, S. Ermon, and B. Poole · 2021
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Tackling the generative learning trilemma with denoising diffusion gans
Z. Xiao, K. Kreis, and A. Vahdat · 2021
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Estimating the optimal covariance with imperfect mean in diffusion probabilistic models
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V. Stimper, D. Liu, A. Campbell, V. Berenz, L. Ryll, B. Schölkopf, and J. M. Hernández-Lobato · 2023
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J. Heek, E. Hoogeboom, and T. Salimans · 2024
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Consistency trajectory models: Learning probability flow ODE trajectory of diffusion
D. Kim, C.-H. Lai, W.-H. Liao, N. Murata, Y. Takida, T. Uesaka, Y. He, Y. Mitsufuji, and S. Ermon · 2024
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Analytic-dpm: an analytic estimate of the optimal reverse variance in diffusion probabilistic models
F. Bao, C. Li, J. Zhu, and B. Zhang · 2022
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Elucidating the design space of diffusion-based generative models
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Pseudo numerical methods for diffusion models on manifolds
L. Liu, Y. Ren, Z. Lin, and Z. Zhao · 2022
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Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps
C. Lu, Y. Zhou, F. Bao, J. Chen, C. Li, and J. Zhu · 2022
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High-resolution image synthesis with latent diffusion models
R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer · 2022
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Progressive distillation for fast sampling of diffusion models
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Improved techniques for training consistency models
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Prolificdreamer: High-fidelity and diverse text-to-3d generation with variational score distillation
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Em distillation for one-step diffusion models
S. Xie, Z. Xiao, D. Kingma, T. Hou, Y. N. Wu, K. P. Murphy, T. Salimans, B. Poole, and R. Gao · 2024
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Improved distribution matching distillation for fast image synthesis
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Hierarchical semi-implicit variational inference with application to diffusion model acceleration
L. Yu, T. Xie, Y. Zhu, T. Yang, X. Zhang, and C. Zhang · 2024
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Normalizing flows are capable generative models
S. Zhai, R. Zhang, P. Nakkiran, D. Berthelot, J. Gu, H. Zheng, T. Chen, M. A. Bautista, N. Jaitly, and J. Susskind · 2024
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Score identity distillation: Exponentially fast distillation of pretrained diffusion models for one-step generation
M. Zhou, H. Zheng, Z. Wang, M. Yin, and H. Huang · 2024
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Distributional diffusion models with scoring rules, 2025
V. D. Bortoli, A. Galashov, J. S. Guntupalli, G. Zhou, K. Murphy, A. Gretton, and A. Doucet · 2025
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Training neural samplers with reverse diffusive kl divergence
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Improving probabilistic diffusion models with optimal covariance matching
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One-step diffusion models with
Y. Xu, W. Nie, and A. Vahdat · 2025
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Towards training one-step diffusion models without distillation
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Inductive moment matching
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Adversarial score identity distillation: Rapidly surpassing the teacher in one step
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