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Text-to-image generative models have enabled high-resolution image synthesis across different domains, but require users to specify the content they wish to generate.
Least squares quantization in pcm
Stuart Lloyd · 1982
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A tutorial on energy-based learning
Yann LeCun, Sumit Chopra, Raia Hadsell, M Ranzato, and Fujie Huang · 2006
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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A connection between score matching and denoising autoencoders
Pascal Vincent · 2011
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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Variational inference with normalizing flows
Danilo Rezende and Shakir Mohamed · 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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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Scene parsing through ade20k dataset
Bolei Zhou, Hang Zhao, Xavier Puig, Sanja Fidler, Adela Barriuso, and Antonio Torralba · 2017
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Monet: Unsupervised scene decomposition and representation
Christopher P Burgess, Loic Matthey, Nicholas Watters, Rishabh Kabra, Irina Higgins, Matt Botvinick, and Alexander Lerchner · 2019
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Implicit generation and generalization in energy-based models
Yilun Du and Igor Mordatch · 2019
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Ali Jahanian, Lucy Chai, and Phillip Isola · 2019
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Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
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Compositional visual generation with energy based models
Yilun Du, Shuang Li, and Igor Mordatch · 2020
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Generative adversarial networks
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2020
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Ganspace: Discovering interpretable gan controls
Erik Härkönen, Aaron Hertzmann, Jaakko Lehtinen, and Sylvain Paris · 2020
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Object-centric learning with slot attention, 2020
Francesco Locatello, Dirk Weissenborn, Thomas Unterthiner, Aravindh Mahendran, Georg Heigold, Jakob Uszkoreit, Alexey Dosovitskiy, and Thomas Kipf · 2020
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Interpreting the latent space of gans for semantic face editing
Yujun Shen, Jinjin Gu, Xiaoou Tang, and Bolei Zhou · 2020
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Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2020
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Unsupervised learning of compositional energy concepts
Yilun Du, Shuang Li, Yash Sharma, B. Joshua Tenenbaum, and Igor Mordatch · 2021
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Unsupervised discovery of 3d physical objects
Yilun Du, Kevin A. Smith, Tomer Ullman, Joshua B. Tenenbaum, and Jiajun Wu · 2021
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Learning to compose visual relations
Nan Liu, Shuang Li, Yilun Du, Josh Tenenbaum, and Antonio Torralba · 2021
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Glide: Towards photorealistic image generation and editing with text-guided diffusion models
Alex Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever, and Mark Chen · 2021
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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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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 · 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, Burcu Karagol Ayan, S Sara Mahdavi, Rapha Gontijo Lopes, et al · 2022
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What the daam: Interpreting stable diffusion using cross attention
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Controllable and compositional generation with latent-space energy-based models
Weili Nie, Arash Vahdat, and Anima Anandkumar · 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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Stylespace analysis: Disentangled controls for stylegan image generation
Zongze Wu, Dani Lischinski, and Eli Shechtman · 2021
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The stable artist: Steering semantics in diffusion latent space
Manuel Brack, Patrick Schramowski, Felix Friedrich, Dominik Hintersdorf, and Kristian Kersting · 2022
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Instructpix2pix: Learning to follow image editing instructions
Tim Brooks, Aleksander Holynski, and Alexei A Efros · 2022
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Training-free structured diffusion guidance for compositional text-to-image synthesis
Weixi Feng, Xuehai He, Tsu-Jui Fu, Varun Jampani, Arjun Akula, Pradyumna Narayana, Sugato Basu, Xin Eric Wang, and William Yang Wang · 2022
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An image is worth one word: Personalizing text-to-image generation using textual inversion
Rinon Gal, Yuval Alaluf, Yuval Atzmon, Or Patashnik, Amit H Bermano, Gal Chechik, and Daniel Cohen-Or · 2022
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Raphael Tang, Akshat Pandey, Zhiying Jiang, Gefei Yang, Karun Kumar, Jimmy Lin, and Ferhan Ture · 2022
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Uncovering the disentanglement capability in text-to-image diffusion models
Qiucheng Wu, Yujian Liu, Handong Zhao, Ajinkya Kale, Trung Bui, Tong Yu, Zhe Lin, Yang Zhang, and Shiyu Chang · 2022
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Sega: Instructing diffusion using semantic dimensions
Manuel Brack, Felix Friedrich, Dominik Hintersdorf, Lukas Struppek, Patrick Schramowski, and Kristian Kersting · 2023
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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
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Towards enhanced controllability of diffusion models, 2023
Wonwoong Cho, Hareesh Ravi, Midhun Harikumar, Vinh Khuc, Krishna Kumar Singh, Jingwan Lu, David I. Inouye, and Ajinkya Kale · 2023
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Attribute-centric compositional text-to-image generation
Yuren Cong, Martin Renqiang Min, Li Erran Li, Bodo Rosenhahn, and Michael Ying Yang · 2023
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Reduce, reuse, recycle: Compositional generation with energy-based diffusion models and mcmc
Yilun Du, Conor Durkan, Robin Strudel, Joshua B Tenenbaum, Sander Dieleman, Rob Fergus, Jascha Sohl-Dickstein, Arnaud Doucet, and Will Grathwohl · 2023
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Composer: Creative and controllable image synthesis with composable conditions
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Collaborative diffusion for multi-modal face generation and editing
Ziqi Huang, Kelvin CK Chan, Yuming Jiang, and Ziwei Liu · 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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Compositional image generation and manipulation with latent diffusion models, 2023
Changhao Shi, Haomiao Ni, Kai Li, Shaobo Han, Mingfu Liang, Gal Mishne, and Martin Renqiang Min · 2023
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Learning disentangled prompts for compositional image synthesis
Kihyuk Sohn, Albert Shaw, Yuan Hao, Han Zhang, Luisa Polania, Huiwen Chang, Lu Jiang, and Irfan Essa · 2023
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Compositional text-to-image synthesis with attention map control of diffusion models
Ruichen Wang, Zekang Chen, Chen Chen, Jian Ma, Haonan Lu, and Xiaodong Lin · 2023
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Concept algebra for text-controlled vision models
Zihao Wang, Lin Gui, Jeffrey Negrea, and Victor Veitch · 2023
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