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Autoregressive (AR) models, long dominant in language generation, are increasingly applied to image synthesis but are often considered less competitive than Diffusion-based models.
Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2010
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Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2010
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Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
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Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network
Wenzhe Shi, Jose Caballero, Ferenc Huszár, Johannes Totz, Andrew P Aitken, Rob Bishop, Daniel Rueckert, and Zehan Wang · 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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Neural discrete representation learning
Aaron Van Den Oord, Oriol Vinyals, et al · 2017
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Improving language understanding by generative pre-training
Alec Radford · 2018
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Transformers are rnns: Fast autoregressive transformers with linear attention
Angelos Katharopoulos, Apoorv Vyas, Nikolaos Pappas, and François Fleuret · 2020
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Language models are few-shot learners
Ben Mann, N Ryder, M Subbiah, J Kaplan, P Dhariwal, A Neelakantan, P Shyam, G Sastry, A Askell, S Agarwal, et al · 2020
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Taming transformers for high-resolution image synthesis
Patrick Esser, Robin Rombach, and Bjorn Ommer · 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, et al · 2021
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Vector-quantized image modeling with improved vqgan
Jiahui Yu, Xin Li, Jing Yu Koh, Han Zhang, Ruoming Pang, James Qin, Alexander Ku, Yuanzhong Xu, Jason Baldridge, and Yonghui Wu · 2021
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Cm3: A causal masked multimodal model of the internet
Armen Aghajanyan, Bernie Huang, Candace Ross, Vladimir Karpukhin, Hu Xu, Naman Goyal, Dmytro Okhonko, Mandar Joshi, Gargi Ghosh, Mike Lewis, et al · 2022
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Classifier-free diffusion guidance
Jonathan Ho and Tim Salimans · 2022
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Flow matching for generative modeling
Yaron Lipman, Ricky TQ Chen, Heli Ben-Hamu, Maximilian Nickel, and Matt Le · 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 L Denton, Kamyar Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, et al · 2022
Cited alongside, same era.
Progressive distillation for fast sampling of diffusion models
Tim Salimans and Jonathan Ho · 2022
Cited alongside, same era.
All are worth words: A vit backbone for diffusion models
Fan Bao, Shen Nie, Kaiwen Xue, Yue Cao, Chongxuan Li, Hang Su, and Jun Zhu · 2023
Cited alongside, same era.
Improving image generation with better captions
James Betker, Gabriel Goh, Li Jing, Tim Brooks, Jianfeng Wang, Linjie Li, Long Ouyang, Juntang Zhuang, Joyce Lee, Yufei Guo, et al · 2023
Cited alongside, same era.
Emu: Enhancing image generation models using photogenic needles in a haystack
Xiaoliang Dai, Ji Hou, Chih-Yao Ma, Sam Tsai, Jialiang Wang, Rui Wang, Peizhao Zhang, Simon Vandenhende, Xiaofang Wang, Abhimanyu Dubey, et al · 2023
Fluid: Scaling autoregressive text-to-image generative models with continuous tokens, 2024
Lijie Fan, Tianhong Li, Siyang Qin, Yuanzhen Li, Chen Sun, Michael Rubinstein, Deqing Sun, Kaiming He, and Yonglong Tian · 2024
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Seed-x: Multimodal models with unified multi-granularity comprehension and generation
Yuying Ge, Sijie Zhao, Jinguo Zhu, Yixiao Ge, Kun Yi, Lin Song, Chen Li, Xiaohan Ding, and Ying Shan · 2024
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Geneval: An object-focused framework for evaluating text-to-image alignment
Dhruba Ghosh, Hannaneh Hajishirzi, and Ludwig Schmidt · 2024
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Better & faster large language models via multi-token prediction
Fabian Gloeckle, Badr Youbi Idrissi, Baptiste Rozière, David Lopez-Paz, and Gabriel Synnaeve · 2024
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Evaluating text-to-visual generation with image-to-text generation
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Cited alongside, same era.
Longnet: Scaling transformers to 1,000,000,000 tokens
Jiayu Ding, Shuming Ma, Li Dong, Xingxing Zhang, Shaohan Huang, Wenhui Wang, Nanning Zheng, and Furu Wei · 2023
Cited alongside, same era.
Emu video: Factorizing text-to-video generation by explicit image conditioning
Rohit Girdhar, Mannat Singh, Andrew Brown, Quentin Duval, Samaneh Azadi, Sai Saketh Rambhatla, Akbar Shah, Xi Yin, Devi Parikh, and Ishan Misra · 2023
Cited alongside, same era.
Mamba: Linear-time sequence modeling with selective state spaces
Albert Gu and Tri Dao · 2023
Cited alongside, same era.
Elasticdiffusion: Training-free arbitrary size image generation
Moayed Haji-Ali, Guha Balakrishnan, and Vicente Ordonez · 2023
Cited alongside, same era.
Scalecrafter: Tuning-free higher-resolution visual generation with diffusion models
Yingqing He, Shaoshu Yang, Haoxin Chen, Xiaodong Cun, Menghan Xia, Yong Zhang, Xintao Wang, Ran He, Qifeng Chen, and Ying Shan · 2023
Cited alongside, same era.
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 · 2023
Cited alongside, same era.
Scalable diffusion models with transformers
William Peebles and Saining Xie · 2023
Cited alongside, same era.
Zhiqiu Lin, Deepak Pathak, Baiqi Li, Jiayao Li, Xide Xia, Graham Neubig, Pengchuan Zhang, and Deva Ramanan · 2024
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Dongyang Liu, Shitian Zhao, Le Zhuo, Weifeng Lin, Yu Qiao, Hongsheng Li, and Peng Gao · 2024
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Open-magvit2: An open-source project toward democratizing auto-regressive visual generation
Zhuoyan Luo, Fengyuan Shi, Yixiao Ge, Yujiu Yang, Limin Wang, and Ying Shan · 2024
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Star: Scale-wise text-to-image generation via auto-regressive representations
Xiaoxiao Ma, Mohan Zhou, Tao Liang, Yalong Bai, Tiejun Zhao, Huaian Chen, and Yi Jin · 2024
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Movie gen: A cast of media foundation models
Adam Polyak, Amit Zohar, Andrew Brown, Andros Tjandra, Animesh Sinha, Ann Lee, Apoorv Vyas, Bowen Shi, Chih-Yao Ma, Ching-Yao Chuang, et al · 2024
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Freescale: Unleashing the resolution of diffusion models via tuning-free scale fusion
Haonan Qiu, Shiwei Zhang, Yujie Wei, Ruihang Chu, Hangjie Yuan, Xiang Wang, Yingya Zhang, and Ziwei Liu · 2024
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Ultrapixel: Advancing ultra-high-resolution image synthesis to new peaks
Jingjing Ren, Wenbo Li, Haoyu Chen, Renjing Pei, Bin Shao, Yong Guo, Long Peng, Fenglong Song, and Lei Zhu · 2024
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Chameleon: Mixed-modal early-fusion foundation models
Chameleon Team · 2024
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Visual autoregressive modeling: Scalable image generation via next-scale prediction
Keyu Tian, Yi Jiang, Zehuan Yuan, Bingyue Peng, and Liwei Wang · 2024
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Janus: Decoupling visual encoding for unified multimodal understanding and generation
Chengyue Wu, Xiaokang Chen, Zhiyu Wu, Yiyang Ma, Xingchao Liu, Zizheng Pan, Wen Liu, Zhenda Xie, Xingkai Yu, Chong Ruan, et al · 2024
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Randomized autoregressive visual generation
Qihang Yu, Ju He, Xueqing Deng, Xiaohui Shen, and Liang-Chieh Chen · 2024
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Transfusion: Predict the next token and diffuse images with one multi-modal model
Chunting Zhou, Lili Yu, Arun Babu, Kushal Tirumala, Michihiro Yasunaga, Leonid Shamis, Jacob Kahn, Xuezhe Ma, Luke Zettlemoyer, and Omer Levy · 2024
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Transfer between modalities with metaqueries
Xichen Pan, Satya Narayan Shukla, Aashu Singh, Zhuokai Zhao, Shlok Kumar Mishra, Jialiang Wang, Zhiyang Xu, Jiuhai Chen, Kunpeng Li, Felix Juefei-Xu, et al · 2025
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