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Methods for finetuning generative models for concept-driven personalization generally achieve strong results for subject-driven or style-driven generation.
Image quilting for texture synthesis and transfer
Alexei A Efros and William T Freeman · 2001
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Image analogies
Aaron Hertzmann, Charles E. Jacobs, Nuria Oliver, Brian Curless, and D. Salesin · 2001
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Training generative adversarial networks with limited data
Tero Karras, Miika Aittala, Janne Hellsten, Samuli Laine, Jaakko Lehtinen, and Timo Aila · 2006
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Image style transfer using convolutional neural networks
Leon A Gatys, Alexander S Ecker, and Matthias Bethge · 2016
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Perceptual losses for real-time style transfer and super-resolution
Justin Johnson, Alexandre Alahi, and Li Fei-Fei · 2016
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Compressed sensing using generative models
Ashish Bora, Ajil Jalal, Eric Price, and Alexandros G. Dimakis · 2017
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Arbitrary style transfer in real-time with adaptive instance normalization
Xun Huang and Serge Belongie · 2017
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Universal style transfer via feature transforms
Yijun Li, Chen Fang, Jimei Yang, Zhaowen Wang, Xin Lu, and Ming-Hsuan Yang · 2017
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A style-based generator architecture for generative adversarial networks
Tero Karras, Samuli Laine, and Timo Aila · 2019
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Arbitrary style transfer with style-attentional networks
Dae Young Park and Kwang Hee Lee · 2019
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and P. Abbeel · 2020
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Analyzing and improving the image quality of stylegan
Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila · 2020
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Pulse: Self-supervised photo upsampling via latent space exploration of generative models
Sachit Menon, Alexandru Damian, Shijia Hu, Nikhil Ravi, and Cynthia Rudin · 2020
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Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2020
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Jojogan: One shot face stylization
Min Jin Chong and David A. Forsyth · 2021
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Clipstyler: Image style transfer with a single text condition
Gihyun Kwon and Jong-Chul Ye · 2021
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Blendgan: Implicitly gan blending for arbitrary stylized face generation
Mingcong Liu, Qiang Li, Zekui Qin, Guoxin Zhang, Pengfei Wan, and Wen Zheng · 2021
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Few-shot image generation via cross-domain correspondence
Utkarsh Ojha, Yijun Li, Jingwan Lu, Alexei A. Efros, Yong Jae Lee, Eli Shechtman, and Richard Zhang · 2021
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High-resolution image synthesis with latent diffusion models
Robin Rombach, A. Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2021
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Mind the gap: Domain gap control for single shot domain adaptation for generative adversarial networks
Peihao Zhu, Rameen Abdal, John Femiani, and Peter Wonka · 2022
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Muse: Text-to-image generation via masked generative transformers
Huiwen Chang, Han Zhang, Jarred Barber, AJ Maschinot, Jose Lezama, Lu Jiang, Ming-Hsuan Yang, Kevin Murphy, William T Freeman, Michael Rubinstein, et al · 2023
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Dreamartist: Towards controllable one-shot text-to-image generation via positive-negative prompt-tuning, 2023
Ziyi Dong, Pengxu Wei, and Liang Lin · 2023
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Mix-of-show: Decentralized low-rank adaptation for multi-concept customization of diffusion models
Yuchao Gu, Xintao Wang, Jay Zhangjie Wu, Yujun Shi, Chen Yunpeng, Zihan Fan, Wuyou Xiao, Rui Zhao, Shuning Chang, Weijia Wu, Yixiao Ge, Shan Ying, and Mike Zheng Shou · 2023
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Towards real-world blind face restoration with generative facial prior
Xintao Wang, Yu Li, Honglun Zhang, and Ying Shan · 2021
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One-shot generative domain adaptation, 2021
Ceyuan Yang, Yujun Shen, Zhiyi Zhang, Yinghao Xu, Jiapeng Zhu, Zhirong Wu, and Bolei Zhou · 2021
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An image is worth one word: Personalizing text-to-image generation using textual inversion, 2022
Rinon Gal, Yuval Alaluf, Yuval Atzmon, Or Patashnik, Amit H. Bermano, Gal Chechik, and Daniel Cohen-Or · 2022
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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, Lu Wang, and Weizhu Chen · 2022
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One-shot adaptation of gan in just one clip
Gihyun Kwon and Jong-Chul Ye · 2022
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Photorealistic text-to-image diffusion models with deep language understanding
Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L. Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, Seyedeh Sara Mahdavi, Raphael Gontijo Lopes, Tim Salimans, Jonathan Ho, David J. Fleet, and Mohammad Norouzi · 2022
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Diffusers: State-of-the-art diffusion models
Patrick von Platen, Suraj Patil, Anton Lozhkov, Pedro Cuenca, Nathan Lambert, Kashif Rasul, Mishig Davaadorj, and Thomas Wolf · 2022
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Ligong Han, Yinxiao Li, Han Zhang, Peyman Milanfar, Dimitris Metaxas, and Feng Yang · 2023
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Gatha: Relational loss for enhancing text-based style transfer
Surgan Jandial, Shripad Deshmukh, Abhinav Java, Simra Shahid, and Balaji Krishnamurthy · 2023
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Multi-concept customization of text-to-image diffusion
Nupur Kumari, Bingliang Zhang, Richard Zhang, Eli Shechtman, and Jun-Yan Zhu · 2023
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Sdxl: Improving latent diffusion models for high-resolution image synthesis
Dustin Podell, Zion English, Kyle Lacey, A. Blattmann, Tim Dockhorn, Jonas Muller, Joe Penna, and Robin Rombach · 2023
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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
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Multistylegan: Multiple one-shot image stylizations using a single gan
Viraj Shah, Ayush Sarkar, Sudharsan Krishnakumar Anita, and Svetlana Lazebnik · 2023
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Styledrop: Text-to-image generation in any style
Kihyuk Sohn, Nataniel Ruiz, Kimin Lee, Daniel Castro Chin, Irina Blok, Huiwen Chang, Jarred Barber, Lu Jiang, Glenn Entis, Yuanzhen Li, et al · 2023
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Merging loras
Simo Ryu · 2024
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MoLE: Mixture of loRA experts
Xun Wu, Shaohan Huang, and Furu Wei · 2024
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