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Diffusion models have marked a significant breakthrough in the synthesis of semantically coherent images.
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Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., Chen, X.: Improved techniques for training gans. Advances in neural information processing systems 29
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Jacob, B., Kligys, S., Chen, B., Zhu, M., Tang, M., Howard, A., Adam, H., Kalenichenko, D.: Quantization and training of neural networks for efficient integer-arithmetic-only inference. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 2704–2713 (2018)
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Banner, R., Nahshan, Y., Soudry, D.: Post training 4-bit quantization of convolutional networks for rapid-deployment. Advances in Neural Information Processing Systems 32
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Nagel, M., Baalen, M.v., Blankevoort, T., Welling, M.: Data-free quantization through weight equalization and bias correction. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 1325–1334 (2019)
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Schmidt, F.: Generalization in generation: A closer look at exposure bias. In: Birch, A., Finch, A., Hayashi, H., Konstas, I., Luong, T., Neubig, G., Oda, Y., Sudoh, K. (eds.) Proceedings of the 3rd Workshop on Neural Generation and Translation. pp. 157–167. Association for Computational Linguistics, Hong Kong (Nov 2019). https://doi.org/10.18653/v1/D19-5616, https://aclanthology.org/D19-5616
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Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. Advances in neural information processing systems 33
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Nagel, M., Amjad, R.A., Van Baalen, M., Louizos, C., Blankevoort, T.: Up or down? adaptive rounding for post-training quantization. In: International Conference on Machine Learning. pp. 7197–7206. PMLR (2020)
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2021
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Dhariwal, P., Nichol, A.: Diffusion models beat gans on image synthesis. Advances in neural information processing systems 34
2021
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2021
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He, Y., Liu, L., Liu, J., Wu, W., Zhou, H., Zhuang, B.: PTQD: Accurate post-training quantization for diffusion models. In: Thirty-seventh Conference on Neural Information Processing Systems (2023), https://openreview.net/forum?id=Y3g1PV5R9l
2023
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Li, X., Liu, Y., Lian, L., Yang, H., Dong, Z., Kang, D., Zhang, S., Keutzer, K.: Q-diffusion: Quantizing diffusion models. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 17535–17545 (2023)
2023
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Li, Y., Xu, S., Cao, X., Sun, X., Zhang, B.: Q-DM: An efficient low-bit quantized diffusion model. In: Thirty-seventh Conference on Neural Information Processing Systems (2023), https://openreview.net/forum?id=sFGkL5BsPi
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Lu, C., Zhou, Y., Bao, F., Chen, J., Li, C., Zhu, J.: Dpm-solver++: Fast solver for guided sampling of diffusion probabilistic models (2023)
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Liu, Z., Wang, Y., Han, K., Zhang, W., Ma, S., Gao, W.: Post-training quantization for vision transformer. Advances in Neural Information Processing Systems 34
2021
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2021
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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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Bao, F., Li, C., Zhu, J., Zhang, B.: Analytic-dpm: an analytic estimate of the optimal reverse variance in diffusion probabilistic models (2022)
2022
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Gholami, A., Kim, S., Dong, Z., Yao, Z., Mahoney, M.W., Keutzer, K.: A survey of quantization methods for efficient neural network inference. In: Low-Power Computer Vision, pp. 291–326. Chapman and Hall/CRC (2022)
2022
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Gu, S., Chen, D., Bao, J., Wen, F., Zhang, B., Chen, D., Yuan, L., Guo, B.: Vector quantized diffusion model for text-to-image synthesis. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 10696–10706 (2022)
2022
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2022
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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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2023
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Ruiz, N., Li, Y., Jampani, V., Pritch, Y., Rubinstein, M., Aberman, K.: Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 22500–22510 (2023)
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Shang, Y., Yuan, Z., Xie, B., Wu, B., Yan, Y.: Post-training quantization on diffusion models. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 1972–1981 (2023)
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So, J., Lee, J., Ahn, D., Kim, H., Park, E.: Temporal dynamic quantization for diffusion models. In: Thirty-seventh Conference on Neural Information Processing Systems (2023), https://openreview.net/forum?id=D1sECc9fiG
2023
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Yang, L., Zhang, Z., Song, Y., Hong, S., Xu, R., Zhao, Y., Zhang, W., Cui, B., Yang, M.H.: Diffusion models: A comprehensive survey of methods and applications. ACM Computing Surveys 56
2023
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Zhang, L., Rao, A., Agrawala, M.: Adding conditional control to text-to-image diffusion models. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 3836–3847 (2023)
2023
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Fang, G., Ma, X., Wang, X.: Structural pruning for diffusion models. Advances in neural information processing systems 36
2024
Closest in time.
He, Y., Liu, J., Wu, W., Zhou, H., Zhuang, B.: EfficientDM: Efficient quantization-aware fine-tuning of low-bit diffusion models. In: The Twelfth International Conference on Learning Representations (2024), https://openreview.net/forum?id=UmMa3UNDAz
2024
Closest in time.
Huang, Y., Gong, R., Liu, J., Chen, T., Liu, X.: Tfmq-dm: Temporal feature maintenance quantization for diffusion models. In: Conference on Computer Vision and Pattern Recognition (CVPR) (2024)
2024
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Li, M., Qu, T., Yao, R., Sun, W., Moens, M.F.: Alleviating exposure bias in diffusion models through sampling with shifted time steps. In: The Twelfth International Conference on Learning Representations (2024), https://openreview.net/forum?id=ZSD3MloKe6
2024
Closest in time.
Li, Y., van der Schaar, M.: On error propagation of diffusion models. In: The Twelfth International Conference on Learning Representations (2024), https://openreview.net/forum?id=RtAct1E2zS
2024
Closest in time.
2024
Closest in time.
Ning, M., Li, M., Su, J., Salah, A.A., Ertugrul, I.O.: Elucidating the exposure bias in diffusion models. In: The Twelfth International Conference on Learning Representations (2024), https://openreview.net/forum?id=xEJMoj1SpX
2024
Closest in time.
Wang, H., Shang, Y., Yuan, Z., Wu, J., Yan, Y.: Quest: Low-bit diffusion model quantization via efficient selective finetuning (2024)
2024
Closest in time.