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Text-to-image (T2I) models are well known for their ability to produce highly realistic images, while multimodal large language models (MLLMs) are renowned for their proficiency in understanding and integrating multiple modalities.
Microsoft coco: Common objects in context, 2015
Tsung-Yi Lin, Michael Maire, Serge Belongie, Lubomir Bourdev, Ross Girshick, James Hays, Pietro Perona, Deva Ramanan, C. Lawrence Zitnick, and Piotr Dollár · 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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A style-based generator architecture for generative adversarial networks, 2019
Tero Karras, Samuli Laine, and Timo Aila · 2019
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Zero: Memory optimizations toward training trillion parameter models, 2020
Samyam Rajbhandari, Jeff Rasley, Olatunji Ruwase, and Yuxiong He · 2020
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Taming transformers for high-resolution image synthesis, 2021
Patrick Esser, Robin Rombach, and Björn Ommer · 2021
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Lora: Low-rank adaptation of large language models, 2021
Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen · 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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Learning transferable visual models from natural language supervision, 2021
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, 2021
Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever · 2021
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Flamingo: a visual language model for few-shot learning, 2022
Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech, Iain Barr, Yana Hasson, Karel Lenc, Arthur Mensch, Katie Millican, Malcolm Reynolds, Roman Ring, Eliza Rutherford, Serkan Cabi, Tengda Han, Zhitao Gong, Sina Samangooei, Marianne Monteiro, Jacob Menick, Sebastian Borgeaud, Andrew Brock, Aida Nematzadeh, Sahand Sharifzadeh, Mikolaj Binkowski, Ricardo Barreira, Oriol Vinyals, Andrew Zisserman, and Karen Simonyan · 2022
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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 · 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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Laion-5b: An open large-scale dataset for training next generation image-text models, 2022
Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Wortsman, Patrick Schramowski, Srivatsa Kundurthy, Katherine Crowson, Ludwig Schmidt, Robert Kaczmarczyk, and Jenia Jitsev · 2022
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Attention distillation: self-supervised vision transformer students need more guidance, 2022
Kai Wang, Fei Yang, and Joost van de Weijer · 2022
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Scaling autoregressive models for content-rich text-to-image generation, 2022
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
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Qwen-vl: A versatile vision-language model for understanding, localization, text reading, and beyond, 2023
Jinze Bai, Shuai Bai, Shusheng Yang, Shijie Wang, Sinan Tan, Peng Wang, Junyang Lin, Chang Zhou, and Jingren Zhou · 2023
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ediff-i: Text-to-image diffusion models with an ensemble of expert denoisers, 2023
Yogesh Balaji, Seungjun Nah, Xun Huang, Arash Vahdat, Jiaming Song, Qinsheng Zhang, Karsten Kreis, Miika Aittala, Timo Aila, Samuli Laine, Bryan Catanzaro, Tero Karras, and Ming-Yu Liu · 2023
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Pick-a-pic: An open dataset of user preferences for text-to-image generation, 2023
Yuval Kirstain, Adam Polyak, Uriel Singer, Shahbuland Matiana, Joe Penna, and Omer Levy · 2023
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Multi-concept customization of text-to-image diffusion, 2023
Nupur Kumari, Bingliang Zhang, Richard Zhang, Eli Shechtman, and Jun-Yan Zhu · 2023
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Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models, 2023
Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi · 2023
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Visual instruction tuning, 2023
Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee · 2023
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T2i-adapter: Learning adapters to dig out more controllable ability for text-to-image diffusion models, 2023
Chong Mou, Xintao Wang, Liangbin Xie, Yanze Wu, Jian Zhang, Zhongang Qi, Ying Shan, and Xiaohu Qie · 2023
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Scalable diffusion models with transformers, 2023
William Peebles and Saining Xie · 2023
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Sdxl: Improving latent diffusion models for high-resolution image synthesis, 2023
Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, and Robin Rombach · 2023
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Exploring the limits of transfer learning with a unified text-to-text transformer, 2023
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu · 2023
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Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation, 2023
Nataniel Ruiz, Yuanzhen Li, Varun Jampani, Yael Pritch, Michael Rubinstein, and Kfir Aberman · 2023
Cited alongside, same era.
Llama 2: Open foundation and fine-tuned chat models, 2023
Hugo Touvron, Louis Martin, and Kevin Stone · 2023
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Deepseek-vl: Towards real-world vision-language understanding, 2024
Haoyu Lu, Wen Liu, Bo Zhang, Bingxuan Wang, Kai Dong, Bo Liu, Jingxiang Sun, Tongzheng Ren, Zhuoshu Li, Hao Yang, Yaofeng Sun, Chengqi Deng, Hanwei Xu, Zhenda Xie, and Chong Ruan · 2024
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Qwen2vl-flux: Unifying image and text guidance for controllable image generation, 2024
Pengqi Lu · 2024
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Subject-diffusion: Open domain personalized text-to-image generation without test-time fine-tuning
Jian Ma, Junhao Liang, Chen Chen, and Haonan Lu · 2024
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Kosmos-g: Generating images in context with multimodal large language models, 2024
Xichen Pan, Li Dong, Shaohan Huang, Zhiliang Peng, Wenhu Chen, and Furu Wei · 2024
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Controlnext: Powerful and efficient control for image and video generation, 2024
Bohao Peng, Jian Wang, Yuechen Zhang, Wenbo Li, Ming-Chang Yang, and Jiaya Jia · 2024
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Human preference score v2: A solid benchmark for evaluating human preferences of text-to-image synthesis, 2023
Xiaoshi Wu, Yiming Hao, Keqiang Sun, Yixiong Chen, Feng Zhu, Rui Zhao, and Hongsheng Li · 2023
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Imagereward: Learning and evaluating human preferences for text-to-image generation, 2023
Jiazheng Xu, Xiao Liu, Yuchen Wu, Yuxuan Tong, Qinkai Li, Ming Ding, Jie Tang, and Yuxiao Dong · 2023
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Controllable textual inversion for personalized text-to-image generation, 2023
Jianan Yang, Haobo Wang, Yanming Zhang, Ruixuan Xiao, Sai Wu, Gang Chen, and Junbo Zhao · 2023
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Ip-adapter: Text compatible image prompt adapter for text-to-image diffusion models
Hu Ye, Jun Zhang, Sibo Liu, Xiao Han, and Wei Yang · 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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Phi-3 technical report: A highly capable language model locally on your phone, 2024
Marah Abdin and et al Jyoti Aneja, Hany Awadalla · 2024
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Mumu: Bootstrapping multimodal image generation from text-to-image data, 2024
William Berman and Alexander Peysakhovich · 2024
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Seededit: Align image re-generation to image editing, 2024
Yichun Shi, Peng Wang, and Weilin Huang · 2024
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Does representation matter? exploring intermediate layers in large language models, 2024
Oscar Skean, Md Rifat Arefin, Yann LeCun, and Ravid Shwartz-Ziv · 2024
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Kolors: Effective training of diffusion model for photorealistic text-to-image synthesis
Kolors Team · 2024
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Dreamomni: Unified image generation and editing, 2024
Bin Xia, Yuechen Zhang, Jingyao Li, Chengyao Wang, Yitong Wang, Xinglong Wu, Bei Yu, and Jiaya Jia · 2024
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Sana: Efficient high-resolution image synthesis with linear diffusion transformers, 2024
Enze Xie, Junsong Chen, Junyu Chen, Han Cai, Haotian Tang, Yujun Lin, Zhekai Zhang, Muyang Li, Ligeng Zhu, Yao Lu, and Song Han · 2024
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Raphael: Text-to-image generation via large mixture of diffusion paths, 2024
Zeyue Xue, Guanglu Song, Qiushan Guo, Boxiao Liu, Zhuofan Zong, Yu Liu, and Ping Luo · 2024
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Anyedit: Mastering unified high-quality image editing for any idea, 2024
Qifan Yu, Wei Chow, Zhongqi Yue, Kaihang Pan, Yang Wu, Xiaoyang Wan, Juncheng Li, Siliang Tang, Hanwang Zhang, and Yueting Zhuang · 2024
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Anygpt: Unified multimodal llm with discrete sequence modeling, 2024
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Trajectory consistency distillation: Improved latent consistency distillation by semi-linear consistency function with trajectory mapping, 2024
Jianbin Zheng, Minghui Hu, Zhongyi Fan, Chaoyue Wang, Changxing Ding, Dacheng Tao, and Tat-Jen Cham · 2024
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Slimflow: Training smaller one-step diffusion models with rectified flow, 2024
Yuanzhi Zhu, Xingchao Liu, and Qiang Liu · 2024
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Easyref: Omni-generalized group image reference for diffusion models via multimodal llm, 2024
Zhuofan Zong, Dongzhi Jiang, Bingqi Ma, Guanglu Song, Hao Shao, Dazhong Shen, Yu Liu, and Hongsheng Li · 2024
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Hangliang Ding, Dacheng Li, Runlong Su, Peiyuan Zhang, Zhijie Deng, Ion Stoica, and Hao Zhang · 2025
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T2i-compbench++: An enhanced and comprehensive benchmark for compositional text-to-image generation
Kaiyi Huang, Chengqi Duan, Kaiyue Sun, Enze Xie, Zhenguo Li, and Xihui Liu · 2025
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