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Recent advancements in timestep-distilled diffusion models have enabled high-quality image generation that rivals non-distilled multi-step models, but with significantly fewer inference steps.
Distilling the knowledge in a neural network
Geoffrey Hinton · 2015
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Dcfnet: Deep neural network with decomposed convolutional filters
Qiang Qiu, Xiuyuan Cheng, Guillermo Sapiro, et al · 2018
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On the efficacy of knowledge distillation
Jang Hyun Cho and Bharath Hariharan · 2019
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Stochastic conditional generative networks with basis decomposition
Ze Wang, Xiuyuan Cheng, Guillermo Sapiro, and Qiang Qiu · 2019
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2020
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Graph convolution with low-rank learnable local filters
Xiuyuan Cheng, Zichen Miao, and Qiang Qiu · 2021
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Lora: Low-rank adaptation of large language models
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, and Weizhu Chen · 2021
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Improved denoising diffusion probabilistic models
Alexander Quinn Nichol and Prafulla Dhariwal · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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Classifier-free diffusion guidance
Jonathan Ho and Tim Salimans · 2022
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Diffstyler: Controllable dual diffusion for text-driven image stylization
Nisha Huang, Yuxin Zhang, Fan Tang, Chongyang Ma, Haibin Huang, Yong Zhang, Weiming Dong, and Changsheng Xu · 2022
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Elucidating the design space of diffusion-based generative models
Tero Karras, Miika Aittala, Timo Aila, and Samuli Laine · 2022
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Pseudo numerical methods for diffusion models on manifolds
Luping Liu, Yi Ren, Zhijie Lin, and Zhou Zhao · 2022
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Pokemon blip captions
Justin NM Pinkney · 2022
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High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
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Progressive distillation for fast sampling of diffusion models
Tim Salimans and Jonathan Ho · 2022
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Laion-5b: An open large-scale dataset for training next generation image-text models
Christoph Schuhmann, Romain Beaumont, Cade W Gordon, Ross Wightman, Theo Coombes, Aarush Katta, Clayton Mullis, Patrick Schramowski, Srivatsa R Kundurthy, Katherine Crowson, et al · 2022
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Scaling autoregressive models for content-rich text-to-image generation
Jiahui Yu, Yuanzhong Xu, Jing Yu Koh, Thang Luong, Gunjan Baid, Zirui Wang, Vijay Vasudevan, Alexander Ku, Yinfei Yang, Burcu Karagol Ayan, et al · 2022
Earlier work this paper cites.
Training diffusion models with reinforcement learning
Kevin Black, Michael Janner, Yilun Du, Ilya Kostrikov, and Sergey Levine · 2023
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Dpok: Reinforcement learning for fine-tuning text-to-image diffusion models
Ying Fan, Olivia Watkins, Yuqing Du, Hao Liu, Moonkyung Ryu, Craig Boutilier, Pieter Abbeel, Mohammad Ghavamzadeh, Kangwook Lee, and Kimin Lee · 2023
Cited alongside, same era.
Svdiff: Compact parameter space for diffusion fine-tuning
Ligong Han, Yinxiao Li, Han Zhang, Peyman Milanfar, Dimitris Metaxas, and Feng Yang · 2023
Cited alongside, same era.
Globalmapper: Arbitrary-shaped urban layout generation
Liu He and Daniel Aliaga · 2023
Cited alongside, same era.
Diffusion-based document layout generation
Liu He, Yijuan Lu, John Corring, Dinei Florencio, and Cha Zhang · 2023
Cited alongside, same era.
Consistency trajectory models: Learning probability flow ode trajectory of diffusion
Dongjun Kim, Chieh-Hsin Lai, Wei-Hsiang Liao, Naoki Murata, Yuhta Takida, Toshimitsu Uesaka, Yutong He, Yuki Mitsufuji, and Stefano Ermon · 2023
Cited alongside, same era.
Qlora: Efficient finetuning of quantized llms
Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, and Luke Zettlemoyer · 2024
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Diffusion-rpo: Aligning diffusion models through relative preference optimization
Yi Gu, Zhendong Wang, Yueqin Yin, Yujia Xie, and Mingyuan Zhou · 2024
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Coho: Context-sensitive city-scale hierarchical urban layout generation
Liu He and Daniel Aliaga · 2024
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Margin-aware preference optimization for aligning diffusion models without reference
Jiwoo Hong, Sayak Paul, Noah Lee, Kashif Rasul, James Thorne, and Jongheon Jeong · 2024
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Imagine flash: Accelerating emu diffusion models with backward distillation
Jonas Kohler, Albert Pumarola, Edgar Schönfeld, Artsiom Sanakoyeu, Roshan Sumbaly, Peter Vajda, and Ali Thabet · 2024
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Pick-a-pic: An open dataset of user preferences for text-to-image generation
Yuval Kirstain, Adam Polyak, Uriel Singer, Shahbuland Matiana, Joe Penna, and Omer Levy · 2023
Cited alongside, same era.
Multi-concept customization of text-to-image diffusion
Nupur Kumari, Bingliang Zhang, Richard Zhang, Eli Shechtman, and Jun-Yan Zhu · 2023
Cited alongside, same era.
Latent consistency models: Synthesizing high-resolution images with few-step inference
Simian Luo, Yiqin Tan, Longbo Huang, Jian Li, and Hang Zhao · 2023
Cited alongside, same era.
Learning to retain while acquiring: Combating distribution-shift in adversarial data-free knowledge distillation
Gaurav Patel, Konda Reddy Mopuri, and Qiang Qiu · 2023
Cited alongside, same era.
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
Cited alongside, same era.
Controlling text-to-image diffusion by orthogonal finetuning
Zeju Qiu, Weiyang Liu, Haiwen Feng, Yuxuan Xue, Yao Feng, Zhen Liu, Dan Zhang, Adrian Weller, and Bernhard Schölkopf · 2023
Cited alongside, same era.
Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation
Nataniel Ruiz, Yuanzhen Li, Varun Jampani, Yael Pritch, Michael Rubinstein, and Kfir Aberman · 2023
Cited alongside, same era.
Closest in time.
Cumulative difference learning vae for time-series with temporally correlated inflow-outflow
Tianchun Li, Chengxiang Wu, Pengyi Shi, and Xiaoqian Wang · 2024
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Step-aware preference optimization: Aligning preference with denoising performance at each step
Zhanhao Liang, Yuhui Yuan, Shuyang Gu, Bohan Chen, Tiankai Hang, Ji Li, and Liang Zheng · 2024
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Sdxl-lightning: Progressive adversarial diffusion distillation
Shanchuan Lin, Anran Wang, and Xiao Yang · 2024
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Training diffusion models towards diverse image generation with reinforcement learning
Zichen Miao, Jiang Wang, Ze Wang, Zhengyuan Yang, Lijuan Wang, Qiang Qiu, and Zicheng Liu · 2024
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Efficient source-free time-series adaptation via parameter subspace disentanglement
Gaurav Patel, Christopher Sandino, Behrooz Mahasseni, Ellen L Zippi, Erdrin Azemi, Ali Moin, and Juri Minxha · 2024
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Direct preference optimization: Your language model is secretly a reward model
Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn · 2024
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Hyper-sd: Trajectory segmented consistency model for efficient image synthesis
Yuxi Ren, Xin Xia, Yanzuo Lu, Jiacheng Zhang, Jie Wu, Pan Xie, Xing Wang, and Xuefeng Xiao · 2024
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Fast high-resolution image synthesis with latent adversarial diffusion distillation
Axel Sauer, Frederic Boesel, Tim Dockhorn, Andreas Blattmann, Patrick Esser, and Robin Rombach · 2024
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Diffusion model alignment using direct preference optimization
Bram Wallace, Meihua Dang, Rafael Rafailov, Linqi Zhou, Aaron Lou, Senthil Purushwalkam, Stefano Ermon, Caiming Xiong, Shafiq Joty, and Nikhil Naik · 2024
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Haoyu Wang, Tianci Liu, Ruirui Li, Monica Cheng, Tuo Zhao, and Jing Gao · 2024
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Groundingbooth: Grounding text-to-image customization
Zhexiao Xiong, Wei Xiong, Jing Shi, He Zhang, Yizhi Song, and Nathan Jacobs · 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
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Self-play fine-tuning of diffusion models for text-to-image generation
Huizhuo Yuan, Zixiang Chen, Kaixuan Ji, and Quanquan Gu · 2024
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Unipc: A unified predictor-corrector framework for fast sampling of diffusion models
Wenliang Zhao, Lujia Bai, Yongming Rao, Jie Zhou, and Jiwen Lu · 2024
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Neat: Nonlinear parameter-efficient adaptation of pre-trained models
Yibo Zhong, Haoxiang Jiang, Lincan Li, Ryumei Nakada, Tianci Liu, Linjun Zhang, Huaxiu Yao, and Haoyu Wang · 2024
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