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The field of advanced text-to-image generation is witnessing the emergence of unified frameworks that integrate powerful text encoders, such as CLIP and T5, with Diffusion Transformer backbones.
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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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Conceptual 12m: Pushing web-scale image-text pre-training to recognize long-tail visual concepts
Soravit Changpinyo, Piyush Sharma, Nan Ding, and Radu Soricut · 2021
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An image is worth 16x16 words: Transformers for image recognition at scale, 2021
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 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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mt5: A massively multilingual pre-trained text-to-text transformer
Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, and Colin Raffel · 2021
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An image is worth one word: Personalizing text-to-image generation using textual inversion, 2022
Rinon Gal, Yuval Alaluf, Yuval Atzmon, Or Patashnik, Amit H. Bermano, Gal Chechik, and Daniel Cohen-Or · 2022
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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 · 2022
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Elucidating the design space of diffusion-based generative models, 2022
Tero Karras, Miika Aittala, Timo Aila, and Samuli Laine · 2022
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Flow straight and fast: Learning to generate and transfer data with rectified flow, 2022
Xingchao Liu, Chengyue Gong, and Qiang Liu · 2022
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Sdedit: Guided image synthesis and editing with stochastic differential equations, 2022
Chenlin Meng, Yutong He, Yang Song, Jiaming Song, Jiajun Wu, Jun-Yan Zhu, and Stefano Ermon · 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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M-vader: A model for diffusion with multimodal context, 2022
Samuel Weinbach, Marco Bellagente, Constantin Eichenberg, Andrew Dai, Robert Baldock, Souradeep Nanda, Björn Deiseroth, Koen Oostermeijer, Hannah Teufel, and Andres Felipe Cruz-Salinas · 2022
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Qwen-vl: A frontier large vision-language model with versatile abilities
Jinze Bai, Shuai Bai, Shusheng Yang, Shijie Wang, Sinan Tan, Peng Wang, Junyang Lin, Chang Zhou, and Jingren Zhou · 2023
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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
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DeepFloyd IF, 2023
DeepFloyd · 2023
Cited alongside, same era.
Dreamllm: Synergistic multimodal comprehension and creation
Runpei Dong, Chunrui Han, Yuang Peng, Zekun Qi, Zheng Ge, Jinrong Yang, Liang Zhao, Jianjian Sun, Hongyu Zhou, Haoran Wei, Xiangwen Kong, Xiangyu Zhang, Kaisheng Ma, and Li Yi · 2023
Cited alongside, same era.
Geneval: An object-focused framework for evaluating text-to-image alignment, 2023
Dhruba Ghosh, Hanna Hajishirzi, and Ludwig Schmidt · 2023
Cited alongside, same era.
Generating images with multimodal language models
Jing Yu Koh, Daniel Fried, and Ruslan Salakhutdinov · 2023
Cited alongside, same era.
Blip-diffusion: Pre-trained subject representation for controllable text-to-image generation and editing, 2023
Minigpt-5: Interleaved vision-and-language generation via generative vokens, 2023
Kaizhi Zheng, Xuehai He, and Xin Eric Wang · 2023
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Announcing black forest labs, 2024
BlackForestLabs · 2024
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Scaling rectified flow transformers for high-resolution image synthesis
Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Müller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, et al · 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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Mini-gemini: Mining the potential of multi-modality vision language models, 2024
Yanwei Li, Yuechen Zhang, Chengyao Wang, Zhisheng Zhong, Yixin Chen, Ruihang Chu, Shaoteng Liu, and Jiaya Jia · 2024
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Dongxu Li, Junnan Li, and Steven C. H. Hoi · 2023
Cited alongside, same era.
Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee · 2023
Cited alongside, same era.
Scalable diffusion models with transformers, 2023
William Peebles and Saining Xie · 2023
Cited alongside, same era.
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.
Generative pretraining in multimodality, 2023
Quan Sun, Qiying Yu, Yufeng Cui, Fan Zhang, Xiaosong Zhang, Yueze Wang, Hongcheng Gao, Jingjing Liu, Tiejun Huang, and Xinlong Wang · 2023
Cited alongside, same era.
Next-gpt: Any-to-any multimodal llm
Shengqiong Wu, Hao Fei, Leigang Qu, Wei Ji, and Tat-Seng Chua · 2023
Cited alongside, same era.
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
Cited alongside, same era.
Hao Liu, Wilson Yan, Matei Zaharia, and Pieter Abbeel · 2024
Later among the works it cites.
Subject-diffusion:open domain personalized text-to-image generation without test-time fine-tuning, 2024
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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SDXL: Improving latent diffusion models for high-resolution image synthesis
Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, and Robin Rombach · 2024
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Chameleon: Mixed-modal early-fusion foundation models
Chameleon Team · 2024
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Cambrian-1: A fully open, vision-centric exploration of multimodal llms, 2024
Shengbang Tong, Ellis Brown, Penghao Wu, Sanghyun Woo, Manoj Middepogu, Sai Charitha Akula, Jihan Yang, Shusheng Yang, Adithya Iyer, Xichen Pan, Austin Wang, Rob Fergus, Yann LeCun, and Saining Xie · 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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Show-o: One single transformer to unify multimodal understanding and generation
Jinheng Xie, Weijia Mao, Zechen Bai, David Junhao Zhang, Weihao Wang, Kevin Qinghong Lin, Yuchao Gu, Zhijie Chen, Zhenheng Yang, and Mike Zheng Shou · 2024
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Anygpt: Unified multimodal llm with discrete sequence modeling
Jun Zhan, Junqi Dai, Jiasheng Ye, Yunhua Zhou, Dong Zhang, Zhigeng Liu, Xin Zhang, Ruibin Yuan, Ge Zhang, Linyang Li, Hang Yan, Jie Fu, Tao Gui, Tianxiang Sun, Yugang Jiang, and Xipeng Qiu · 2024
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