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Diffusion models have recently achieved remarkable progress in generating realistic images.
Auto-encoding variational bayes
Diederik P. Kingma and Max Welling · 2014
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An improved non-monotonic transition system for dependency parsing
Matthew Honnibal and Mark Johnson · 2015
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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 E. Reed, Zeynep Akata, Xinchen Yan, Lajanugen Logeswaran, Bernt Schiele, and Honglak Lee · 2016
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AttnGAN: Fine-grained text to image generation with attentional generative adversarial networks
Tao Xu, Pengchuan Zhang, Qiuyuan Huang, Han Zhang, Zhe Gan, Xiaolei Huang, and Xiaodong He · 2018
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DM-GAN: Dynamic memory generative adversarial networks for text-to-image synthesis
Minfeng Zhu, Pingbo Pan, Wei Chen, and Yi Yang · 2019
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Diffusion models beat GANs on image synthesis
Prafulla Dhariwal and Alexander Quinn Nichol · 2021
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CogView: Mastering text-to-image generation via transformers
Ming Ding, Zhuoyi Yang, Wenyi Hong, Wendi Zheng, Chang Zhou, Da Yin, Junyang Lin, Xu Zou, Zhou Shao, Hongxia Yang, and Jie Tang · 2021
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CLIPScore: A reference-free evaluation metric for image captioning
Jack Hessel, Ari Holtzman, Maxwell Forbes, Ronan Le Bras, and Yejin Choi · 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, Gretchen Krueger, and Ilya Sutskever · 2021
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Zero-shot text-to-image generation
Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever · 2021
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Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2021
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eDiff-I: Text-to-image diffusion models with an ensemble of expert denoisers
Yogesh Balaji, Seungjun Nah, Xun Huang, Arash Vahdat, Jiaming Song, Karsten Kreis, Miika Aittala, Timo Aila, Samuli Laine, Bryan Catanzaro, Tero Karras, and Ming-Yu Liu · 2022
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Make-A-Scene: Scene-based text-to-image generation with human priors
Oran Gafni, Adam Polyak, Oron Ashual, Shelly Sheynin, Devi Parikh, and Yaniv Taigman · 2022
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Benchmarking spatial relationships in text-to-image generation
Tejas Gokhale, Hamid Palangi, Besmira Nushi, Vibhav Vineet, Eric Horvitz, Ece Kamar, Chitta Baral, and Yezhou Yang · 2022
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GLIDE: Towards photorealistic image generation and editing with text-guided diffusion models
Alexander Quinn Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever, and Mark Chen · 2022
Cited alongside, same era.
Hierarchical text-conditional image generation with CLIP latents
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen · 2022
Cited alongside, same era.
High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
Cited alongside, same era.
ReCLIP: A strong zero-shot baseline for referring expression comprehension
Sanjay Subramanian, William Merrill, Trevor Darrell, Matt Gardner, Sameer Singh, and Anna Rohrbach · 2022
Cited alongside, same era.
DF-GAN: A simple and effective baseline for text-to-image synthesis
Ming Tao, Hao Tang, Fei Wu, Xiaoyuan Jing, Bing-Kun Bao, and Changsheng Xu · 2022
Cited alongside, same era.
TIFA: accurate and interpretable text-to-image faithfulness evaluation with question answering
Yushi Hu, Benlin Liu, Jungo Kasai, Yizhong Wang, Mari Ostendorf, Ranjay Krishna, and Noah A. Smith · 2023
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GLIGEN: Open-set grounded text-to-image generation
Yuheng Li, Haotian Liu, Qingyang Wu, Fangzhou Mu, Jianwei Yang, Jianfeng Gao, Chunyuan Li, and Yong Jae Lee · 2023
Closest in time.
Magic3D: High-resolution text-to-3d content creation
Chen-Hsuan Lin, Jun Gao, Luming Tang, Towaki Takikawa, Xiaohui Zeng, Xun Huang, Karsten Kreis, Sanja Fidler, Ming-Yu Liu, and Tsung-Yi Lin · 2023
Closest in time.
OpenAI · 2023
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Grounded text-to-image synthesis with attention refocusing
Quynh Phung, Songwei Ge, and Jia-Bin Huang · 2023
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ReCo: Region-controlled text-to-image generation
Zhengyuan Yang, Jianfeng Wang, Zhe Gan, Linjie Li, Kevin Lin, Chenfei Wu, Nan Duan, Zicheng Liu, Ce Liu, Michael Zeng, and Lijuan Wang · 2022
Cited alongside, same era.
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.
SpaText: Spatio-textual representation for controllable image generation
Omri Avrahami, Thomas Hayes, Oran Gafni, Sonal Gupta, Yaniv Taigman, Devi Parikh, Dani Lischinski, Ohad Fried, and Xi Yin · 2023
Cited alongside, same era.
HRS-Bench: Holistic, reliable and scalable benchmark for text-to-image models
Eslam Mohamed Bakr, Pengzhan Sun, Xiaoqian Shen, Faizan Farooq Khan, Li Erran Li, and Mohamed Elhoseiny · 2023
Cited alongside, same era.
Generative AI for immersive experiences: Integrating text-to-image models in VR-mediated co-design workflows
Chris Bussell, Ahmed Ehab, Daniel Hartle-Ryan, and Timo Kapsalis · 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.
Training-free layout control with cross-attention guidance
Minghao Chen, Iro Laina, and Andrea Vedaldi · 2023
Cited alongside, same era.
Closest in time.
DreamFusion: Text-to-3D using 2D diffusion
Ben Poole, Ajay Jain, Jonathan T. Barron, and Ben Mildenhall · 2023
Closest in time.
LayoutLLM-T2I: Eliciting layout guidance from LLM for text-to-image generation
Leigang Qu, Shengqiong Wu, Hao Fei, Liqiang Nie, and Tat-Seng Chua · 2023
Closest in time.
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
Closest in time.
BoxDiff: Text-to-image synthesis with training-free box-constrained diffusion
Jinheng Xie, Yuexiang Li, Yawen Huang, Haozhe Liu, Wentian Zhang, Yefeng Zheng, and Mike Zheng Shou · 2023
Closest in time.
Freestyle layout-to-image synthesis
Han Xue, Zhiwu Huang, Qianru Sun, Li Song, and Wenjun Zhang · 2023
Closest in time.
Adding conditional control to text-to-image diffusion models
Lvmin Zhang and Maneesh Agrawala · 2023
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Continuous layout editing of single images with diffusion models
Zhiyuan Zhang, Zhitong Huang, and Jing Liao · 2023
Closest in time.
Layoutdiffusion: Controllable diffusion model for layout-to-image generation
Guangcong Zheng, Xianpan Zhou, Xuewei Li, Zhongang Qi, Ying Shan, and Xi Li · 2023
Closest in time.
Analyzing and mitigating object hallucination in large vision-language models
Yiyang Zhou, Chenhang Cui, Jaehong Yoon, Linjun Zhang, Zhun Deng, Chelsea Finn, Mohit Bansal, and Huaxiu Yao · 2023
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LLM-grounded diffusion: Enhancing prompt understanding of text-to-image diffusion models with large language models
Long Lian, Boyi Li, Adam Yala, and Trevor Darrell · 2024
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