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Custom diffusion models (CDMs) have attracted widespread attention due to their astonishing generative ability for personalized concepts.
U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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Generative adversarial text to image synthesis
Scott Reed, Zeynep Akata, Xinchen Yan, Lajanugen Logeswaran, Bernt Schiele, and Honglak Lee · 2016
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Image-to-image translation with conditional adversarial networks
Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A. Efros · 2017
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Overcoming catastrophic forgetting in neural networks
James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, et al · 2017
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Learning without forgetting
Zhizhong Li and Derek Hoiem · 2017
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icarl: Incremental classifier and representation learning
Sylvestre-Alvise Rebuffi, Alexander Kolesnikov, Georg Sperl, and Christoph H. Lampert · 2017
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Continual learning with deep generative replay
Hanul Shin, Jung Kwon Lee, Jaehong Kim, and Jiwon Kim · 2017
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Lifelong learning with dynamically expandable networks
Jaehong Yoon, Eunho Yang, Jeongtae Lee, and Sung Ju Hwang · 2018
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Compacting, picking and growing for unforgetting continual learning
Steven C. Y. Hung, Cheng-Hao Tu, Cheng-En Wu, Chien-Hung Chen, Yi-Ming Chan, and Chu-Song Chen · 2019
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Semantic image synthesis with spatially-adaptive normalization
Taesung Park, Ming-Yu Liu, Ting-Chun Wang, and Jun-Yan Zhu · 2019
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Memory-efficient incremental learning through feature adaptation
Ahmet Iscen, Jeffrey Zhang, Svetlana Lazebnik, and Cordelia Schmid · 2020
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Classifier-free diffusion guidance
Jonathan Ho and Tim Salimans · 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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Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2021
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Federated class-incremental learning
Jiahua Dong, Lixu Wang, Zhen Fang, Gan Sun, Shichao Xu, Xiao Wang, and Qi Zhu · 2022
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Dytox: Transformers for continual learning with dynamic token expansion
Arthur Douillard, Alexandre Ramé, Guillaume Couairon, and Matthieu Cord · 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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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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Elucidating the design space of diffusion-based generative models
Tero Karras, Miika Aittala, Timo Aila, and Samuli Laine · 2022
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Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation
Junnan Li, Dongxu Li, Caiming Xiong, and Steven Hoi · 2022
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DiffuseVAE: Efficient, controllable and high-fidelity generation from low-dimensional latents
Kushagra Pandey, Avideep Mukherjee, Piyush Rai, and Abhishek Kumar · 2022
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Hierarchical text-conditional image generation with clip latents
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen · 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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Photorealistic text-to-image diffusion models with deep language understanding
Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Raphael Gontijo-Lopes, Burcu Karagol Ayan, Tim Salimans, Jonathan Ho, David J. Fleet, and Mohammad Norouzi · 2022
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Learning to imagine: Diversify memory for incremental learning using unlabeled data
Yujin Tang, Yifan Peng, and Weishi Zheng · 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, Ben Hutchinson, Wei Han, Zarana Parekh, Xin Li, Han Zhang, Jason Baldridge, and Yonghui Wu · 2022
Cited alongside, same era.
Attend-and-excite: Attention-based semantic guidance for text-to-image diffusion models
Hila Chefer, Yuval Alaluf, Yael Vinker, Lior Wolf, and Daniel Cohen-Or · 2023
Cited alongside, same era.
Subject-driven text-to-image generation via apprenticeship learning
Wenhu Chen, Hexiang Hu, Yandong Li, Nataniel Ruiz, Xuhui Jia, Ming-Wei Chang, and William W Cohen · 2023
Cited alongside, same era.
Anydoor: Zero-shot object-level image customization
Xi Chen, Lianghua Huang, Yu Liu, Yujun Shen, Deli Zhao, and Hengshuang Zhao · 2023
Elite: Encoding visual concepts into textual embeddings for customized text-to-image generation
Yuxiang Wei, Yabo Zhang, Zhilong Ji, Jinfeng Bai, Lei Zhang, and Wangmeng Zuo · 2023
Later among the works it cites.
Better aligning text-to-image models with human preference
Xiaoshi Wu, Keqiang Sun, Feng Zhu, Rui Zhao, and Hongsheng Li · 2023
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Continual named entity recognition without catastrophic forgetting
Duzhen Zhang, Wei Cong, Jiahua Dong, Yahan Yu, Xiuyi Chen, Yonggang Zhang, and Zhen Fang · 2023
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Composing parameter-efficient modules with arithmetic operation
Jinghan Zhang, Shiqi Chen, Junteng Liu, and Junxian He · 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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Inversion-based style transfer with diffusion models
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Cited alongside, same era.
Diffusion models in vision: A survey
Florinel-Alin Croitoru, Vlad Hondru, Radu Tudor Ionescu, and Mubarak Shah · 2023
Cited alongside, same era.
Heterogeneous forgetting compensation for class-incremental learning
Jiahua Dong, Wenqi Liang, Yang Cong, and Gan Sun · 2023
Cited alongside, same era.
An image is worth one word: Personalizing text-to-image generation using textual inversion
Rinon Gal, Yuval Alaluf, Yuval Atzmon, Or Patashnik, Amit Haim Bermano, Gal Chechik, and Daniel Cohen-or · 2023
Cited alongside, same era.
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
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.
Taming encoder for zero fine-tuning image customization with text-to-image diffusion models
Xuhui Jia, Yang Zhao, Kelvin C. K. Chan, Yandong Li, Han Zhang, Boqing Gong, Tingbo Hou, Huisheng Wang, and Yu-Chuan Su · 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.
Yuxin Zhang, Nisha Huang, Fan Tang, Haibin Huang, Chongyang Ma, Weiming Dong, and Changsheng Xu · 2023
Later among the works it cites.
Motiondirector: Motion customization of text-to-video diffusion models
Rui Zhao, Yuchao Gu, Jay Zhangjie Wu, David Junhao Zhang, Jiawei Liu, Weijia Wu, Jussi Keppo, and Mike Zheng Shou · 2023
Later among the works it cites.
No one left behind: Real-world federated class-incremental learning
Jiahua Dong, Hongliu Li, Yang Cong, Gan Sun, Yulun Zhang, and Luc Van Gool · 2024
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Ranni: Taming text-to-image diffusion for accurate instruction following
Yutong Feng, Biao Gong, Di Chen, Yujun Shen, Yu Liu, and Jingren Zhou · 2024
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Omg: Occlusion-friendly personalized multi-concept generation in diffusion models
Zhe Kong, Yong Zhang, Tianyu Yang, Tao Wang, Kaihao Zhang, Bizhu Wu, Guanying Chen, Wei Liu, and Wenhan Luo · 2024
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Referring image editing: Object-level image editing via referring expressions
Chang Liu, Xiangtai Li, and Henghui Ding · 2024
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T2i-adapter: Learning adapters to dig out more controllable ability for text-to-image diffusion models
Chong Mou, Xintao Wang, Liangbin Xie, Yanze Wu, Jian Zhang, Zhongang Qi, Ying Shan, and Xiaohu Qie · 2024
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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 · 2024
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A survey of multimodal-guided image editing with text-to-image diffusion models
Xincheng Shuai, Henghui Ding, Xingjun Ma, Rongcheng Tu, Yu-Gang Jiang, and Dacheng Tao · 2024
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Continual diffusion: Continual customization of text-to-image diffusion with c-lora
James Seale Smith, Yen-Chang Hsu, Lingyu Zhang, Ting Hua, Zsolt Kira, Yilin Shen, and Hongxia Jin · 2024
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Create your world: Lifelong text-to-image diffusion
Gan Sun, Wenqi Liang, Jiahua Dong, Jun Li, Zhengming Ding, and Yang Cong · 2024
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Texttoucher: Fine-grained text-to-touch generation
Jiahang Tu, Hao Fu, Fengyu Yang, Hanbin Zhao, Chao Zhang, and Hui Qian · 2024
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Driveditfit: Fine-tuning diffusion transformers for autonomous driving
Jiahang Tu, Wei Ji, Hanbin Zhao, Chao Zhang, Roger Zimmermann, and Hui Qian · 2024
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Apisr: Anime production inspired real-world anime super-resolution
Boyang Wang, Fengyu Yang, Xihang Yu, Chao Zhang, and Hanbin Zhao · 2024
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Mixture of loRA experts
Xun Wu, Shaohan Huang, and Furu Wei · 2024
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Yang Yang, Wen Wang, Liang Peng, Chaotian Song, Yao Chen, Hengjia Li, Xiaolong Yang, Qinglin Lu, Deng Cai, Boxi Wu, and Wei Liu · 2024
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MM-LLMs: Recent advances in MultiModal large language models
Duzhen Zhang, Yahan Yu, Jiahua Dong, Chenxing Li, Dan Su, Chenhui Chu, and Dong Yu · 2024
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Multi-lora composition for image generation
Ming Zhong, Yelong Shen, Shuohang Wang, Yadong Lu, Yizhu Jiao, Siru Ouyang, Donghan Yu, Jiawei Han, and Weizhu Chen · 2024
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