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Ultra-high-resolution image generation poses great challenges, such as increased semantic planning complexity and detail synthesis difficulties, alongside substantial training resource demands.
Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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Image super-resolution using deep convolutional networks
Chao Dong, Chen Change Loy, Kaiming He, and Xiaoou Tang · 2015
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Improved techniques for training gans
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 2016
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
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Second-order attention network for single image super-resolution
Tao Dai, Jianrui Cai, Yongbing Zhang, Shu-Tao Xia, and Lei Zhang · 2019
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2020
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Label-efficient semantic segmentation with diffusion models
Dmitry Baranchuk, Ivan Rubachev, Andrey Voynov, Valentin Khrulkov, and Artem Babenko · 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, et al · 2021
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Taming transformers for high-resolution image synthesis
Patrick Esser, Robin Rombach, and Bjorn Ommer · 2021
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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 · 2021
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Swinir: Image restoration using swin transformer
Jingyun Liang, Jiezhang Cao, Guolei Sun, Kai Zhang, Luc Van Gool, and Radu Timofte · 2021
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Nerf: Representing scenes as neural radiance fields for view synthesis
Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng · 2021
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Real-esrgan: Training real-world blind super-resolution with pure synthetic data
Xintao Wang, Liangbin Xie, Chao Dong, and Ying Shan · 2021
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Designing a practical degradation model for deep blind image super-resolution
Kai Zhang, Jingyun Liang, Luc Van Gool, and Radu Timofte · 2021
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Transformers as meta-learners for implicit neural representations
Yinbo Chen and Xiaolong Wang · 2022
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Imagen video: High definition video generation with diffusion models
Jonathan Ho, William Chan, Chitwan Saharia, Jay Whang, Ruiqi Gao, Alexey Gritsenko, Diederik P Kingma, Ben Poole, Mohammad Norouzi, David J Fleet, et al · 2022
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Classifier-free diffusion guidance
Jonathan Ho and Tim Salimans · 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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Audioldm: Text-to-audio generation with latent diffusion models
Haohe Liu, Zehua Chen, Yi Yuan, Xinhao Mei, Xubo Liu, Danilo Mandic, Wenwu Wang, and Mark D Plumbley · 2023
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Midjourney v6, 2023
Midjourney · 2023
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Dall-e 3, 2023
OpenAI · 2023
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Scalable diffusion models with transformers
William Peebles and Saining Xie · 2023
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Würstchen: An efficient architecture for large-scale text-to-image diffusion models
Pablo Pernias, Dominic Rampas, Mats Leon Richter, Christopher Pal, and Marc Aubreville · 2023
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Sdxl: Improving latent diffusion models for high-resolution image synthesis
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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, Kamyar Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, et al · 2022
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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
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Lion: Latent point diffusion models for 3d shape generation
Arash Vahdat, Francis Williams, Zan Gojcic, Or Litany, Sanja Fidler, Karsten Kreis, et al · 2022
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Multidiffusion: Fusing diffusion paths for controlled image generation
Omer Bar-Tal, Lior Yariv, Yaron Lipman, and Tali Dekel · 2023
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Reproducible scaling laws for contrastive language-image learning
Mehdi Cherti, Romain Beaumont, Ross Wightman, Mitchell Wortsman, Gabriel Ilharco, Cade Gordon, Christoph Schuhmann, Ludwig Schmidt, and Jenia Jitsev · 2023
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Demofusion: Democratising high-resolution image generation with no $$$
Ruoyi Du, Dongliang Chang, Timothy Hospedales, Yi-Zhe Song, and Zhanyu Ma · 2023
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Elasticdiffusion: Training-free arbitrary size image generation
Moayed Haji-Ali, Guha Balakrishnan, and Vicente Ordonez · 2023
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Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, and Robin Rombach · 2023
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Open-vocabulary panoptic segmentation with text-to-image diffusion models
Jiarui Xu, Sifei Liu, Arash Vahdat, Wonmin Byeon, Xiaolong Wang, and Shalini De Mello · 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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Shen Zhang, Zhaowei Chen, Zhenyu Zhao, Zhenyuan Chen, Yao Tang, Yuhao Chen, Wengang Cao, and Jiajun Liang · 2023
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Resadapter: Domain consistent resolution adapter for diffusion models
Jiaxiang Cheng, Pan Xie, Xin Xia, Jiashi Li, Jie Wu, Yuxi Ren, Huixia Li, Xuefeng Xiao, Min Zheng, and Lean Fu · 2024
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Fouriscale: A frequency perspective on training-free high-resolution image synthesis
Linjiang Huang, Rongyao Fang, Aiping Zhang, Guanglu Song, Si Liu, Yu Liu, and Hongsheng Li · 2024
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Training-free diffusion model adaptation for variable-sized text-to-image synthesis
Zhiyu Jin, Xuli Shen, Bin Li, and Xiangyang Xue · 2024
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Pick-a-pic: An open dataset of user preferences for text-to-image generation
Yuval Kirstain, Adam Polyak, Uriel Singer, Shahbuland Matiana, Joe Penna, and Omer Levy · 2024
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Playground v2. 5: Three insights towards enhancing aesthetic quality in text-to-image generation
Daiqing Li, Aleks Kamko, Ehsan Akhgari, Ali Sabet, Linmiao Xu, and Suhail Doshi · 2024
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Diffusion hyperfeatures: Searching through time and space for semantic correspondence
Grace Luo, Lisa Dunlap, Dong Huk Park, Aleksander Holynski, and Trevor Darrell · 2024
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