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Latent Diffusion Models (LDMs) capture the dynamic evolution of latent variables over time, blending patterns and multimodality in a generative system.
Bbdm: Image-to-image translation with brownian bridge diffusion models
Bo Li, Kaitao Xue, Bin Liu, and Yu-Kun Lai · 1961
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Modern digital and analog communication systems
Bhagwandas P Lathi · 1990
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Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
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U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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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
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Hashgan: Deep learning to hash with pair conditional wasserstein gan
Yue Cao, Bin Liu, Mingsheng Long, and Jianmin Wang · 2018
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Quantizing deep convolutional networks for efficient inference: A whitepaper
Raghuraman Krishnamoorthi · 2018
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Quantization for rapid deployment of deep neural networks
Jun Haeng Lee, Sangwon Ha, Saerom Choi, Won-Jo Lee, and Seungwon Lee · 2018
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A quantization-friendly separable convolution for mobilenets
Tao Sheng, Chen Feng, Shaojie Zhuo, Xiaopeng Zhang, Liang Shen, and Mickey Aleksic · 2018
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Lq-nets: Learned quantization for highly accurate and compact deep neural networks
Dongqing Zhang, Jiaolong Yang, Dongqiangzi Ye, and Gang Hua · 2018
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Post training 4-bit quantization of convolutional networks for rapid-deployment
Ron Banner, Yury Nahshan, and Daniel Soudry · 2019
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Hawq: Hessian aware quantization of neural networks with mixed-precision
Zhen Dong, Zhewei Yao, Amir Gholami, Michael W Mahoney, and Kurt Keutzer · 2019
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Additive powers-of-two quantization: An efficient non-uniform discretization for neural networks
Yuhang Li, Xin Dong, and Wei Wang · 2019
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Haq: Hardware-aware automated quantization with mixed precision
Kuan Wang, Zhijian Liu, Yujun Lin, Ji Lin, and Song Han · 2019
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Zeroq: A novel zero shot quantization framework
Yaohui Cai, Zhewei Yao, Zhen Dong, Amir Gholami, Michael W Mahoney, and Kurt Keutzer · 2020
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Effectively unbiased fid and inception score and where to find them
Min Jin Chong and David Forsyth · 2020
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Reliable fidelity and diversity metrics for generative models
Muhammad Ferjad Naeem, Seong Joon Oh, Youngjung Uh, Yunjey Choi, and Jaejun Yoo · 2020
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Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2020
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Differentiable joint pruning and quantization for hardware efficiency
Ying Wang, Yadong Lu, and Tijmen Blankevoort · 2020
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Easyquant: Post-training quantization via scale optimization
Outlier suppression: Pushing the limit of low-bit transformer language models
Xiuying Wei, Yunchen Zhang, Xiangguo Zhang, Ruihao Gong, Shanghang Zhang, Qi Zhang, Fengwei Yu, and Xianglong Liu · 2022
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Shifting machine learning for healthcare from development to deployment and from models to data
Angela Zhang, Lei Xing, James Zou, and Joseph C Wu · 2022
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Token merging for fast stable diffusion
Daniel Bolya and Judy Hoffman · 2023
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Structural pruning for diffusion models
Gongfan Fang, Xinyin Ma, and Xinchao Wang · 2023
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Ptqd: Accurate post-training quantization for diffusion models
Yefei He, Luping Liu, Jing Liu, Weijia Wu, Hong Zhou, and Bohan Zhuang · 2023
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Di Wu, Qi Tang, Yongle Zhao, Ming Zhang, Ying Fu, and Debing Zhang · 2020
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Towards mixed-precision quantization of neural networks via constrained optimization
Weihan Chen, Peisong Wang, and Jian Cheng · 2021
Cited alongside, same era.
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
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Improved denoising diffusion probabilistic models
Alexander Quinn Nichol and Prafulla Dhariwal · 2021
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Towards efficient post-training quantization of pre-trained language models
Haoli Bai, Lu Hou, Lifeng Shang, Xin Jiang, Irwin King, and Michael R Lyu · 2022
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Vector quantized diffusion model for text-to-image synthesis
Shuyang Gu, Dong Chen, Jianmin Bao, Fang Wen, Bo Zhang, Dongdong Chen, Lu Yuan, and Baining Guo · 2022
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Mr. biq: Post-training non-uniform quantization based on minimizing the reconstruction error
Yongkweon Jeon, Chungman Lee, Eulrang Cho, and Yeonju Ro · 2022
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Image guided inpainting with parameter efficient learning
Sangbeom Lim and Seungryong Kim · 2023
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On distillation of guided diffusion models
Chenlin Meng, Robin Rombach, Ruiqi Gao, Diederik Kingma, Stefano Ermon, Jonathan Ho, and Tim Salimans · 2023
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Early exiting for accelerated inference in diffusion models
Taehong Moon, Moonseok Choi, EungGu Yun, Jongmin Yoon, Gayoung Lee, and Juho Lee · 2023
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Sdxl: Improving latent diffusion models for high-resolution image synthesis
Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, and Robin Rombach · 2023
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Post-training quantization on diffusion models
Yuzhang Shang, Zhihang Yuan, Bin Xie, Bingzhe Wu, and Yan Yan · 2023
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Consistency models
Yang Song, Prafulla Dhariwal, Mark Chen, and Ilya Sutskever · 2023
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Plug-and-play diffusion features for text-driven image-to-image translation
Narek Tumanyan, Michal Geyer, Shai Bagon, and Tali Dekel · 2023
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Towards accurate data-free quantization for diffusion models
Changyuan Wang, Ziwei Wang, Xiuwei Xu, Yansong Tang, Jie Zhou, and Jiwen Lu · 2023
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Smoothquant: Accurate and efficient post-training quantization for large language models
Guangxuan Xiao, Ji Lin, Mickael Seznec, Hao Wu, Julien Demouth, and Song Han · 2023
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Adding conditional control to text-to-image diffusion models
Lvmin Zhang, Anyi Rao, and Maneesh Agrawala · 2023
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