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Flow matching (FM) is a general framework for defining probability paths via Ordinary Differential Equations (ODEs) to transform between noise and data samples.
Springer, 2009
C. Villani · 2009
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D. Rezende and S. Mohamed, “Variational inference with normalizing flows,” in
2015
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John Wiley & Sons, 2016
J. C. Butcher, · 2016
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M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter, “Gans trained by a two time-scale update rule converge to a local nash equilibrium,”
2017
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R. T. Chen, Y. Rubanova, J. Bettencourt, and D. K. Duvenaud, “Neural ordinary differential equations,”
2018
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D. P. Kingma and P. Dhariwal, “Glow: Generative flow with invertible 1x1 convolutions,”
2018
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Y. Song and S. Ermon, “Generative modeling by estimating gradients of the data distribution,”
2019
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R. T. Chen, J. Behrmann, D. K. Duvenaud, and J.-H. Jacobsen, “Residual flows for invertible generative modeling,”
2019
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Z. Xiao, Q. Yan, and Y. Amit, “Generative latent flow,”
2019
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J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” in
2020
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A. Makkuva, A. Taghvaei, S. Oh, and J. Lee, “Optimal transport mapping via input convex neural networks,” in
2020
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Y. Song, J. Sohl-Dickstein, D. P. Kingma, A. Kumar, S. Ermon, and B. Poole, “Score-based generative modeling through stochastic differential equations,” in
2020
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J. Song, C. Meng, and S. Ermon, “Denoising diffusion implicit models,” in
2020
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Y. Choi, Y. Uh, J. Yoo, and J.-W. Ha, “Stargan v2: Diverse image synthesis for multiple domains,” in
2020
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Y. Song, C. Durkan, I. Murray, and S. Ermon, “Maximum likelihood training of score-based diffusion models,”
2021
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F. Bao, C. Li, J. Zhu, and B. Zhang, “Analytic-dpm: an analytic estimate of the optimal reverse variance in diffusion probabilistic models,” in
2021
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T. Dockhorn, A. Vahdat, and K. Kreis, “Score-based generative modeling with critically-damped langevin diffusion,” in
2021
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A. Tong, N. Malkin, G. Huguet, Y. Zhang, J. Rector-Brooks, K. FATRAS, G. Wolf, and Y. Bengio, “Improving and generalizing flow-based generative models with minibatch optimal transport,” in
2023
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A.-A. Pooladian, H. Ben-Hamu, C. Domingo-Enrich, B. Amos, Y. Lipman, and R. T. Chen, “Multisample flow matching: Straightening flows with minibatch couplings,” 2023
2023
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Y. Song, P. Dhariwal, M. Chen, and I. Sutskever, “Consistency models,” in
2023
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D. Kim, C.-H. Lai, W.-H. Liao, N. Murata, Y. Takida, T. Uesaka, Y. He, Y. Mitsufuji, and S. Ermon, “Consistency trajectory models: Learning probability flow ode trajectory of diffusion,” in
2023
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L. Klein, A. Krämer, and F. Noe, “Equivariant flow matching,” in
2023
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2021
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A. Vahdat, K. Kreis, and J. Kautz, “Score-based generative modeling in latent space,”
2021
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M. Grcić, I. Grubišić, and S. Šegvić, “Densely connected normalizing flows,”
2021
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Y. Lipman, R. T. Chen, H. Ben-Hamu, M. Nickel, and M. Le, “Flow matching for generative modeling,” in
2022
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M. S. Albergo and E. Vanden-Eijnden, “Building normalizing flows with stochastic interpolants,” in
2022
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X. Liu, C. Gong,
2022
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T. Dockhorn, A. Vahdat, and K. Kreis, “GENIE: Higher-Order Denoising Diffusion Solvers,”
2022
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H. Zheng, W. Nie, A. Vahdat, K. Azizzadenesheli, and A. Anandkumar, “Fast sampling of diffusion models via operator learning,” in
2023
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W. Luo, “A comprehensive survey on knowledge distillation of diffusion models,”
2023
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Y. Song and P. Dhariwal, “Improved techniques for training consistency models,”
2023
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S. Lee, B. Kim, and J. C. Ye, “Minimizing trajectory curvature of ode-based generative models,” in
2023
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2023
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L. Yang, Z. Yu, C. Meng, M. Xu, S. Ermon, and B. Cui, “Mastering text-to-image diffusion: Recaptioning, planning, and generating with multimodal llms,” in
2024
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L. Yang, Z. Zhang, Z. Yu, J. Liu, M. Xu, S. Ermon, and B. CUI, “Cross-modal contextualized diffusion models for text-guided visual generation and editing,” in
2024
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2024
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B. Nguyen, B. Nguyen, and V. A. Nguyen, “Bellman optimal stepsize straightening of flow-matching models,” in
2024
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2024
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2024
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W. Luo, T. Hu, S. Zhang, J. Sun, Z. Li, and Z. Zhang, “Diff-instruct: A universal approach for transferring knowledge from pre-trained diffusion models,”
2024
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