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Flow matching as a paradigm of generative model achieves notable success across various domains.
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De Bortoli, V., Thornton, J., Heng, J., Doucet, A.: Diffusion schrödinger bridge with applications to score-based generative modeling. NeurIPS 34
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Song, Y., Sohl-Dickstein, J., Kingma, D.P., Kumar, A., Ermon, S., Poole, B.: Score-based generative modeling through stochastic differential equations. In: ICLR (2021)
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Karras, T., Aittala, M., Aila, T., Laine, S.: Elucidating the design space of diffusion-based generative models. NeurIPS (2022)
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Kwon, D., Fan, Y., Lee, K.: Score-based generative modeling secretly minimizes the wasserstein distance. In: NeurIPS. vol. 35, pp. 20205–20217 (2022)
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Lu, C., Zhou, Y., Bao, F., Chen, J., Li, C., Zhu, J.: Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps. In: NeurIPS. vol. 35, pp. 5775–5787 (2022)
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Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: CVPR. pp. 10684–10695 (2022)
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Salimans, T., Ho, J.: Progressive distillation for fast sampling of diffusion models. In: ICLR (2022)
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Xiao, Z., Kreis, K., Vahdat, A.: Tackling the generative learning trilemma with denoising diffusion gans. In: ICLR (2022)
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Tong, A., Malkin, N., Huguet, G., Zhang, Y., Rector-Brooks, J., Fatras, K., Wolf, G., Bengio, Y.: Improving and generalizing flow-based generative models with minibatch optimal transport. In: ICMLW (2023)
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Yu, J., Wang, Y., Zhao, C., Ghanem, B., Zhang, J.: Freedom: Training-free energy-guided conditional diffusion model. In: ICCV (2023)
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Zhang, Q., Chen, Y.: Fast sampling of diffusion models with exponential integrator. In: ICLR (2023)
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Zhao, W., Bai, L., Rao, Y., Zhou, J., Lu, J.: Unipc: A unified predictor-corrector framework for fast sampling of diffusion models. In: NeurIPS (2023)
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Karras, T., Aittala, M., Aila, T., Laine, S.: Elucidating the design space of diffusion-based generative models. NeurIPS (2022)
2022
Cited alongside, same era.
2022
Cited alongside, same era.
Kawar, B., Elad, M., Ermon, S., Song, J.: Denoising diffusion restoration models. NeurIPS 35
2022
Cited alongside, same era.
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: CVPR. pp. 10684–10695 (2022)
2022
Cited alongside, same era.
Salimans, T., Ho, J.: Progressive distillation for fast sampling of diffusion models. In: ICLR (2022)
2022
Cited alongside, same era.
Albergo, M.S., Vanden-Eijnden, E.: Building normalizing flows with stochastic interpolants. In: ICLR (2023)
2023
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2023
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Lee, S., Kim, B., Ye, J.C.: Minimizing trajectory curvature of ode-based generative models. In: ICML (2023)
2023
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Lipman, Y., Chen, R.T., Ben-Hamu, H., Nickel, M., Le, M.: Flow matching for generative modeling. In: ICLR (2023)
2023
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Liu, X., Gong, C., et al.: Flow straight and fast: Learning to generate and transfer data with rectified flow. In: ICLR (2023)
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Peebles, W., Xie, S.: Scalable diffusion models with transformers. In: ICCV (2023)
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Song, Y., Dhariwal, P., Chen, M., Sutskever, I.: Consistency models. In: ICML (2023)
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Yu, J., Wang, Y., Zhao, C., Ghanem, B., Zhang, J.: Freedom: Training-free energy-guided conditional diffusion model. In: ICCV (2023)
2023
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Alemohammad, S., Casco-Rodriguez, J., Luzi, L., Humayun, A.I., Babaei, H., LeJeune, D., Siahkoohi, A., Baraniuk, R.G.: Self-consuming generative models go mad. In: ICLR (2024)
2024
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Gao, R., Hoogeboom, E., Heek, J., Bortoli, V.D., Murphy, K.P., Salimans, T.: Diffusion meets flow matching: Two sides of the same coin (2024), https://diffusionflow.github.io/
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Liu, X., Zhang, X., Ma, J., Peng, J., Liu, Q.: Instaflow: One step is enough for high-quality diffusion-based text-to-image generation. In: ICLR (2024)
2024
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Nguyen, B., Nguyen, B., Nguyen, V.A.: Bellman optimal step-size straightening of flow-matching models. In: ICLR (2024)
2024
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Xue, S., Liu, Z., Chen, F., Zhang, S., Hu, T., Xie, E., Li, Z.: Accelerating diffusion sampling with optimized time steps. In: CVPR (2024)
2024
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Zhang, H., Zhou, J., Lu, Y., Guo, M., Wang, P., Shen, L., Qu, Q.: The emergence of reproducibility and consistency in diffusion models. In: ICML (2024)
2024
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Zhou, Z., Chen, D., Wang, C., Chen, C.: Fast ode-based sampling for diffusion models in around 5 steps. In: CVPR (2024)
2024
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Zhang, H., Zhou, J., Lu, Y., Guo, M., Wang, P., Shen, L., Qu, Q.: The emergence of reproducibility and consistency in diffusion models. In: ICML (2024)
2024
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