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Model quantization is a promising method for accelerating and compressing diffusion models.
Post-training quantization on diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 1972–1981
Yuzhang Shang, Zhihang Yuan, Bin Xie, Bingzhe Wu, and Yan Yan. 2023 · 1981
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition . Ieee, 248–255
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. 2009 · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2014 · 2014
Earlier work this paper cites.
Microsoft coco: Common objects in context. In Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13 . Springer, 740–755
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick. 2014 · 2014
Earlier work this paper cites.
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, 234–241
Olaf Ronneberger, Philipp Fischer, and Thomas Brox. 2015 · 2015
Earlier work this paper cites.
Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop
Fisher Yu, Ari Seff, Yinda Zhang, Shuran Song, Thomas Funkhouser, and Jianxiong Xiao. 2015 · 2015
Earlier work this paper cites.
Improved techniques for training gans
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen. 2016 · 2016
Earlier work this paper cites.
Gans trained by a two time-scale update rule converge to a local nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. 2017 · 2017
Earlier work this paper cites.
Quantization and training of neural networks for efficient integer-arithmetic-only inference. In Proceedings of the IEEE conference on computer vision and pattern recognition . 2704–2713
Benoit Jacob, Skirmantas Kligys, Bo Chen, Menglong Zhu, Matthew Tang, Andrew Howard, Hartwig Adam, and Dmitry Kalenichenko. 2018 · 2018
Earlier work this paper cites.
Relaxed quantization for discretized neural networks
Christos Louizos, Matthias Reisser, Tijmen Blankevoort, Efstratios Gavves, and Max Welling. 2018 · 2018
Earlier work this paper cites.
Learned step size quantization
Steven K Esser, Jeffrey L McKinstry, Deepika Bablani, Rathinakumar Appuswamy, and Dharmendra S Modha. 2019 · 2019
Earlier work this paper cites.
Differentiable soft quantization: Bridging full-precision and low-bit neural networks. In Proceedings of the IEEE/CVF international conference on computer vision . 4852–4861
Ruihao Gong, Xianglong Liu, Shenghu Jiang, Tianxiang Li, Peng Hu, Jiazhen Lin, Fengwei Yu, and Junjie Yan. 2019 · 2019
Earlier work this paper cites.
Data-free quantization through weight equalization and bias correction. In Proceedings of the IEEE/CVF International Conference on Computer Vision . 1325–1334
Markus Nagel, Mart van Baalen, Tijmen Blankevoort, and Max Welling. 2019 · 2019
Earlier work this paper cites.
Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon. 2019 · 2019
Earlier work this paper cites.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel. 2020 · 2020
Earlier work this paper cites.
Up or down? adaptive rounding for post-training quantization. In International Conference on Machine Learning . PMLR, 7197–7206
Markus Nagel, Rana Ali Amjad, Mart Van Baalen, Christos Louizos, and Tijmen Blankevoort. 2020 · 2020
Earlier work this paper cites.
Permutation invariant graph generation via score-based generative modeling. In International Conference on Artificial Intelligence and Statistics . PMLR, 4474–4484
Chenhao Niu, Yang Song, Jiaming Song, Shengjia Zhao, Aditya Grover, and Stefano Ermon. 2020 · 2020
Earlier work this paper cites.
Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon. 2020 · 2020
Earlier work this paper cites.
Clipscore: A reference-free evaluation metric for image captioning
Jack Hessel, Ari Holtzman, Maxwell Forbes, Ronan Le Bras, and Yejin Choi. 2021 · 2021
Earlier work this paper cites.
Brecq: Pushing the limit of post-training quantization by block reconstruction
Yuhang Li, Ruihao Gong, Xu Tan, Yang Yang, Peng Hu, Qi Zhang, Fengwei Yu, Wei Wang, and Shi Gu. 2021 · 2021
Earlier work this paper cites.
Improved denoising diffusion probabilistic models. In International Conference on Machine Learning . PMLR, 8162–8171
Alexander Quinn Nichol and Prafulla Dhariwal. 2021 · 2021
Cited alongside, same era.
Post-training sparsity-aware quantization
Gil Shomron, Freddy Gabbay, Samer Kurzum, and Uri Weiser. 2021 · 2021
Cited alongside, same era.
Imagen video: High definition video generation with diffusion models
Jonathan Ho, William Chan, Chitwan Saharia, Jay Whang, Ruiqi Gao, Alexey Gritsenko, Diederik P Kingma, Ben Poole, Mohammad Norouzi, David J Fleet, et al · 2022
Cited alongside, same era.
Integer-only zero-shot quantization for efficient speech recognition. In ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 4288–4292
Sehoon Kim, Amir Gholami, Zhewei Yao, Nicholas Lee, Patrick Wang, Aniruddha Nrusimha, Bohan Zhai, Tianren Gao, Michael W Mahoney, and Kurt Keutzer. 2022 · 2022
Cited alongside, same era.
Srdiff: Single image super-resolution with diffusion probabilistic models
Patch-wise Mixed-Precision Quantization of Vision Transformer
Junrui Xiao, Zhikai Li, Lianwei Yang, and Qingyi Gu. 2023b · 2023
Later among the works it cites.
Dual Grained Quantization: Efficient Fine-Grained Quantization for LLM
Luoming Zhang, Wen Fei, Weijia Wu, Yefei He, Zhenyu Lou, and Hong Zhou. 2023a · 2023
Later among the works it cites.
Delta-DiT: A Training-Free Acceleration Method Tailored for Diffusion Transformers
Pengtao Chen, Mingzhu Shen, Peng Ye, Jianjian Cao, Chongjun Tu, Christos-Savvas Bouganis, Yiren Zhao, and Tao Chen. 2024 · 2024
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Principled weight initialisation for input-convex neural networks
Pieter-Jan Hoedt and Günter Klambauer. 2024 · 2024
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TCAQ-DM: Timestep-Channel Adaptive Quantization for Diffusion Models
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Haoying Li, Yifan Yang, Meng Chang, Shiqi Chen, Huajun Feng, Zhihai Xu, Qi Li, and Yueting Chen. 2022 · 2022
Cited alongside, same era.
Pseudo numerical methods for diffusion models on manifolds
Luping Liu, Yi Ren, Zhijie Lin, and Zhou Zhao. 2022 · 2022
Cited alongside, same era.
Dpm-solver++: Fast solver for guided sampling of diffusion probabilistic models
Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu. 2022 · 2022
Cited alongside, same era.
High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition . 10684–10695
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. 2022 · 2022
Cited alongside, same era.
Photorealistic text-to-image diffusion models with deep language understanding
Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L Denton, Kamyar Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, et al · 2022
Cited alongside, same era.
Learning fast samplers for diffusion models by differentiating through sample quality
Daniel Watson, William Chan, Jonathan Ho, and Mohammad Norouzi. 2022 · 2022
Cited alongside, same era.
gDDIM: Generalized denoising diffusion implicit models
Qinsheng Zhang, Molei Tao, and Yongxin Chen. 2022 · 2022
Cited alongside, same era.
Cbq: Cross-block quantization for large language models
Xin Ding, Xiaoyu Liu, Zhijun Tu, Yun Zhang, Wei Li, Jie Hu, Hanting Chen, Yehui Tang, Zhiwei Xiong, Baoqun Yin, et al · 2023
Cited alongside, same era.
Haocheng Huang, Jiaxin Chen, Jinyang Guo, Ruiyi Zhan, and Yunhong Wang. 2024a · 2024
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Svdqunat: Absorbing outliers by low-rank components for 4-bit diffusion models
Muyang Li, Yujun Lin, Zhekai Zhang, Tianle Cai, Xiuyu Li, Junxian Guo, Enze Xie, Chenlin Meng, Jun-Yan Zhu, and Song Han. 2024a · 2024
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Zhikai Li, Xuewen Liu, Jing Zhang, and Qingyi Gu. 2024b · 2024
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AWQ: Activation-aware Weight Quantization for On-Device LLM Compression and Acceleration
Ji Lin, Jiaming Tang, Haotian Tang, Shang Yang, Wei-Ming Chen, Wei-Chen Wang, Guangxuan Xiao, Xingyu Dang, Chuang Gan, and Song Han. 2024 · 2024
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Enhanced distribution alignment for post-training quantization of diffusion models
Xuewen Liu, Zhikai Li, Junrui Xiao, and Qingyi Gu. 2024 · 2024
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Deepcache: Accelerating diffusion models for free. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 15762–15772
Xinyin Ma, Gongfan Fang, and Xinchao Wang. 2024 · 2024
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Bitsfusion: 1.99 bits weight quantization of diffusion model
Yang Sui, Yanyu Li, Anil Kag, Yerlan Idelbayev, Junli Cao, Ju Hu, Dhritiman Sagar, Bo Yuan, Sergey Tulyakov, and Jian Ren. 2024 · 2024
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Quest: Low-bit diffusion model quantization via efficient selective finetuning
Haoxuan Wang, Yuzhang Shang, Zhihang Yuan, Junyi Wu, and Yan Yan. 2024b · 2024
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Lavie: High-quality video generation with cascaded latent diffusion models
Yaohui Wang, Xinyuan Chen, Xin Ma, Shangchen Zhou, Ziqi Huang, Yi Wang, Ceyuan Yang, Yinan He, Jiashuo Yu, Peiqing Yang, et al · 2024
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LaVin-DiT: Large Vision Diffusion Transformer
Zhaoqing Wang, Xiaobo Xia, Runnan Chen, Dongdong Yu, Changhu Wang, Mingming Gong, and Tongliang Liu. 2024c · 2024
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Cache me if you can: Accelerating diffusion models through block caching. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 6211–6220
Felix Wimbauer, Bichen Wu, Edgar Schoenfeld, Xiaoliang Dai, Ji Hou, Zijian He, Artsiom Sanakoyeu, Peizhao Zhang, Sam Tsai, Jonas Kohler, et al · 2024
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PTQ4DiT: Post-training Quantization for Diffusion Transformers
Junyi Wu, Haoxuan Wang, Yuzhang Shang, Mubarak Shah, and Yan Yan. 2024 · 2024
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Imagereward: Learning and evaluating human preferences for text-to-image generation
Jiazheng Xu, Xiao Liu, Yuchen Wu, Yuxuan Tong, Qinkai Li, Ming Ding, Jie Tang, and Yuxiao Dong. 2024 · 2024
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K-sort arena: Efficient and reliable benchmarking for generative models via k-wise human preferences. In Proceedings of the Computer Vision and Pattern Recognition Conference . 9131–9141
Zhikai Li, Xuewen Liu, Dongrong Joe Fu, Jianquan Li, Qingyi Gu, Kurt Keutzer, and Zhen Dong. 2025 · 2025
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Cachequant: Comprehensively accelerated diffusion models. In Proceedings of the Computer Vision and Pattern Recognition Conference . 23269–23280
Xuewen Liu, Zhikai Li, and Qingyi Gu. 2025 · 2025
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An analysis of weight initialization methods in connection with different activation functions for feedforward neural networks
Kit Wong, Rolf Dornberger, and Thomas Hanne. 2024 · 2089
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