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Diffusion models are a powerful generative framework, but come with expensive inference.
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Nichol, A.Q., Dhariwal, P.: Improved denoising diffusion probabilistic models. In: International Conference on Machine Learning. pp. 8162–8171. PMLR (2021)
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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. Advances in Neural Information Processing Systems 35
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2023
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Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 10684–10695 (2022)
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Zhang, Q., Chen, Y.: Fast sampling of diffusion models with exponential integrator. In: The Eleventh International Conference on Learning Representations (2022)
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2023
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Song, Y., Dhariwal, P., Chen, M., Sutskever, I.: Consistency models (2023)
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Yang, X., Zhou, D., Feng, J., Wang, X.: Diffusion probabilistic model made slim. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 22552–22562 (2023)
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Zheng, K., Lu, C., Chen, J., Zhu, J.: Dpm-solver-v3: Improved diffusion ode solver with empirical model statistics. In: Thirty-seventh Conference on Neural Information Processing Systems (2023)
2023
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Huang, K., Sun, K., Xie, E., Li, Z., Liu, X.: T2i-compbench: A comprehensive benchmark for open-world compositional text-to-image generation. Advances in Neural Information Processing Systems 36
2024
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Lin, S., Liu, B., Li, J., Yang, X.: Common diffusion noise schedules and sample steps are flawed. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. pp. 5404–5411 (2024)
2024
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