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Diffusion models excel in many generative modeling tasks, notably in creating images from text prompts, a task referred to as text-to-image (T2I) generation.
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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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Bertscore: Evaluating text generation with bert
Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q Weinberger, and Yoav Artzi · 2019
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Recent trends in image watermarking techniques for copyright protection: a survey
Arkadip Ray and Somaditya Roy · 2020
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Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2020
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Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
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Classifier-free diffusion guidance
Jonathan Ho and Tim Salimans · 2021
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Copyright law of the united states (title 17) and related laws contained in title 17 of the united states code, 2022
U.S. Copyright Office · 2022
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Derivative work, 2022
Law School Cornell · 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
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.
Laion-5b: An open large-scale dataset for training next generation image-text models
Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Wortsman, et al · 2022
Cited alongside, same era.
Extracting training data from diffusion models
Nicholas Carlini, Jamie Hayes, Milad Nasr, Matthew Jagielski, Vikash Sehwag, Florian Tramer, Borja Balle, Daphne Ippolito, and Eric Wallace · 2023
Cited alongside, same era.
Measuring the success of diffusion models at imitating human artists
Probabilistic copyright protection can fail for text-to-image generative models
Xiang Li, Qianli Shen, and Kenji Kawaguchi · 2023
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Guides: Copyright and fair use: Transformation: A 5th factor, 2023
Illinois State University Milner Library · 2023
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How long does copyright protection last? (FAQ): U.S. Copyright Office, 2023
U.S. Copyright Office · 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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Diffusion art or digital forgery? investigating data replication in diffusion models
Gowthami Somepalli, Vasu Singla, Micah Goldblum, Jonas Geiping, and Tom Goldstein · 2023
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Stephen Casper, Zifan Guo, Shreya Mogulothu, Zachary Marinov, Chinmay Deshpande, Rui-Jie Yew, Zheng Dai, and Dylan Hadfield-Menell · 2023
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.
Diffusionshield: A watermark for copyright protection against generative diffusion models
Yingqian Cui, Jie Ren, Han Xu, Pengfei He, Hui Liu, Lichao Sun, and Jiliang Tang · 2023
Cited alongside, same era.
Erasing concepts from diffusion models
Rohit Gandikota, Joanna Materzyńska, Jaden Fiotto-Kaufman, and David Bau · 2023
Cited alongside, same era.
Copyright violations and large language models
Antonia Karamolegkou, Jiaang Li, Li Zhou, and Anders Søgaard · 2023
Cited alongside, same era.
How does information bottleneck help deep learning?
Kenji Kawaguchi, Zhun Deng, Xu Ji, and Jiaoyang Huang · 2023
Cited alongside, same era.
Ablating concepts in text-to-image diffusion models
Nupur Kumari, Bingliang Zhang, Sheng-Yu Wang, Eli Shechtman, Richard Zhang, and Jun-Yan Zhu · 2023
Cited alongside, same era.
Understanding and mitigating copying in diffusion models
Gowthami Somepalli, Vasu Singla, Micah Goldblum, Jonas Geiping, and Tom Goldstein · 2023
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AI art tools stable diffusion and Midjourney targeted with copyright lawsuit, 2023 2023
James Vincent · 2023
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Provable copyright protection for generative models
Nikhil Vyas, Sham Kakade, and Boaz Barak · 2023
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Forget-me-not: Learning to forget in text-to-image diffusion models
Eric Zhang, Kai Wang, Xingqian Xu, Zhangyang Wang, and Humphrey Shi · 2023
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A recipe for watermarking diffusion models
Yunqing Zhao, Tianyu Pang, Chao Du, Xiao Yang, Ngai-Man Cheung, and Min Lin · 2023
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Haonan Wang, Qianli Shen, Yao Tong, Yang Zhang, and Kenji Kawaguchi · 2024
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