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Diffusion models have emerged as a promising approach for generating high-quality, high-dimensional images.
Y. Shang, Z. Yuan, B. Xie, B. Wu, and Y. Yan, “Post-training quantization on diffusion models,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2023, pp. 1972–1981
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D. P. Kingma, “Auto-encoding variational bayes,” arXiv preprint arXiv:1312.6114 , 2013
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I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” Advances in neural information processing systems , vol. 27, 2014
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O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18 . Springer, 2015, pp. 234–241
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T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen, “Improved techniques for training gans,” Advances in neural information processing systems , vol. 29, 2016
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M. Arjovsky, S. Chintala, and L. Bottou, “Wasserstein generative adversarial networks,” in International conference on machine learning . PMLR, 2017, pp. 214–223
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I. Higgins, L. Matthey, A. Pal, C. P. Burgess, X. Glorot, M. M. Botvinick, S. Mohamed, and A. Lerchner, “beta-vae: Learning basic visual concepts with a constrained variational framework.” ICLR (Poster) , vol. 3, 2017
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B. Li, X. Qi, T. Lukasiewicz, and P. Torr, “Controllable text-to-image generation,” Advances in neural information processing systems , vol. 32, 2019
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J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” Advances in neural information processing systems , vol. 33, pp. 6840–6851, 2020
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2020
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P. Dhariwal and A. Nichol, “Diffusion models beat gans on image synthesis,” Advances in neural information processing systems , vol. 34, pp. 8780–8794, 2021
2021
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V. Popov, I. Vovk, V. Gogoryan, T. Sadekova, and M. Kudinov, “Grad-tts: A diffusion probabilistic model for text-to-speech,” in International Conference on Machine Learning . PMLR, 2021, pp. 8599–8608
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2021
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Y. Rao, W. Zhao, B. Liu, J. Lu, J. Zhou, and C.-J. Hsieh, “Dynamicvit: Efficient vision transformers with dynamic token sparsification,” Advances in neural information processing systems , vol. 34, pp. 13 937–13 949, 2021
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B. Kawar, M. Elad, S. Ermon, and J. Song, “Denoising diffusion restoration models,” Advances in Neural Information Processing Systems , vol. 35, pp. 23 593–23 606, 2022
2022
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R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-resolution image synthesis with latent diffusion models,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2022, pp. 10 684–10 695
2022
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2022
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X. Li, J. Thickstun, I. Gulrajani, P. S. Liang, and T. B. Hashimoto, “Diffusion-lm improves controllable text generation,” Advances in Neural Information Processing Systems , vol. 35, pp. 4328–4343, 2022
2022
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2023
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S. Gao, X. Liu, B. Zeng, S. Xu, Y. Li, X. Luo, J. Liu, X. Zhen, and B. Zhang, “Implicit diffusion models for continuous super-resolution,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2023, pp. 10 021–10 030
2023
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T. Brooks, A. Holynski, and A. A. Efros, “Instructpix2pix: Learning to follow image editing instructions,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 18 392–18 402
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2023
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2022
Cited alongside, same era.
H. Li, Y. Yang, M. Chang, S. Chen, H. Feng, Z. Xu, Q. Li, and Y. Chen, “Srdiff: Single image super-resolution with diffusion probabilistic models,” Neurocomputing , vol. 479, pp. 47–59, 2022
2022
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O. Avrahami, D. Lischinski, and O. Fried, “Blended diffusion for text-driven editing of natural images,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2022, pp. 18 208–18 218
2022
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2022
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2022
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2022
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C. Saharia, W. Chan, S. Saxena, L. Li, J. Whang, E. L. Denton, K. Ghasemipour, R. Gontijo Lopes, B. Karagol Ayan, T. Salimans et al. , “Photorealistic text-to-image diffusion models with deep language understanding,” Advances in neural information processing systems , vol. 35, pp. 36 479–36 494, 2022
2022
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2022
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C. Meng, R. Rombach, R. Gao, D. Kingma, S. Ermon, J. Ho, and T. Salimans, “On distillation of guided diffusion models,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 14 297–14 306
2023
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2023
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2023
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D. Bolya and J. Hoffman, “Token merging for fast stable diffusion,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2023, pp. 4599–4603
2023
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W. Peebles and S. Xie, “Scalable diffusion models with transformers,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 4195–4205
2023
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Y. Li, H. Wang, Q. Jin, J. Hu, P. Chemerys, Y. Fu, Y. Wang, S. Tulyakov, and J. Ren, “Snapfusion: Text-to-image diffusion model on mobile devices within two seconds,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
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X. Ma, G. Fang, and X. Wang, “Deepcache: Accelerating diffusion models for free,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 15 762–15 772
2024
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2024
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Y. He, L. Liu, J. Liu, W. Wu, H. Zhou, and B. Zhuang, “Ptqd: Accurate post-training quantization for diffusion models,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
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F. Wimbauer, B. Wu, E. Schoenfeld, X. Dai, J. Hou, Z. He, A. Sanakoyeu, P. Zhang, S. Tsai, J. Kohler et al. , “Cache me if you can: Accelerating diffusion models through block caching,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 6211–6220
2024
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2024
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M. Kim, S. Gao, Y.-C. Hsu, Y. Shen, and H. Jin, “Token fusion: Bridging the gap between token pruning and token merging,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , 2024, pp. 1383–1392
2024
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T. Huang, Y. Zhang, M. Zheng, S. You, F. Wang, C. Qian, and C. Xu, “Knowledge diffusion for distillation,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
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A. Shih, S. Belkhale, S. Ermon, D. Sadigh, and N. Anari, “Parallel sampling of diffusion models,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
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