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Text-to-image generation has made significant advancements with the introduction of text-to-image diffusion models.
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. NeurIPS (2014)
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Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft coco: Common objects in context. ECCV (2014)
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Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. NeurIPS (2014)
2014
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Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. MICCAI (2015)
2015
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Reed, S., Akata, Z., Yan, X., Logeswaran, L., Schiele, B., Lee, H.: Generative adversarial text to image synthesis. ICML (2016)
2016
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Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. NeurIPS (2017)
2017
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Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. NeurIPS (2017)
2017
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Zhang, H., Xu, T., Li, H., Zhang, S., Wang, X., Huang, X., Metaxas, D.N.: Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks. ICCV (2017)
2017
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Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. NAACL-HLT (2018)
2018
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Radford, A., Narasimhan, K., Salimans, T., Sutskever, I., et al.: Improving language understanding by generative pre-training. OpenAI blog (2018)
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2019
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Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. ICLR (2019)
2019
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Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. OpenAI blog (2019)
2019
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Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. NeurIPS 2020
2020
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Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. NeurIPS (2020)
2020
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Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., Liu, P.J.: Exploring the limits of transfer learning with a unified text-to-text transformer. JMLR (2020)
2020
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Dhariwal, P., Nichol, A.: Diffusion models beat gans on image synthesis. NeurIPS (2021)
2021
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Ding, M., Yang, Z., Hong, W., Zheng, W., Zhou, C., Yin, D., Lin, J., Zou, X., Shao, Z., Yang, H., et al.: Cogview: Mastering text-to-image generation via transformers. NeurIPS (2021)
2021
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Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. ICLR (2021)
2021
Cited alongside, same era.
Ho, J., Salimans, T.: Classifier-free diffusion guidance. NeurIPS Workshop on Deep Generative Models and Downstream Applications (2021)
2021
Cited alongside, same era.
Nichol, A., Dhariwal, P., Ramesh, A., Shyam, P., Mishkin, P., McGrew, B., Sutskever, I., Chen, M.: Glide: Towards photorealistic image generation and editing with text-guided diffusion models. ICML (2021)
2021
Cited alongside, same era.
Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al.: Learning transferable visual models from natural language supervision. ICML (2021)
2021
Cited alongside, same era.
Croitoru, F.A., Hondru, V., Ionescu, R.T., Shah, M.: Diffusion models in vision: A survey. TPAMI (2023)
2023
Later among the works it cites.
Ge, S., Park, T., Zhu, J.Y., Huang, J.B.: Expressive text-to-image generation with rich text. ICCV (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
Huang, K., Sun, K., Xie, E., Li, Z., Liu, X.: T2i-compbench: A comprehensive benchmark for open-world compositional text-to-image generation. arXiv preprint arXiv: 2307.06350 (2023)
2023
Later among the works it cites.
Kawar, B., Zada, S., Lang, O., Tov, O., Chang, H., Dekel, T., Mosseri, I., Irani, M.: Imagic: Text-based real image editing with diffusion models. CVPR (2023)
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Ramesh, A., Pavlov, M., Goh, G., Gray, S., Voss, C., Radford, A., Chen, M., Sutskever, I.: Zero-shot text-to-image generation. ICML (2021)
2021
Cited alongside, same era.
2021
Cited alongside, same era.
Song, J., Meng, C., Ermon, S.: Denoising diffusion implicit models. ICLR (2021)
2021
Cited alongside, same era.
Song, Y., Sohl-Dickstein, J., Kingma, D.P., Kumar, A., Ermon, S., Poole, B.: Score-based generative modeling through stochastic differential equations. ICLR (2021)
2021
Cited alongside, same era.
2022
Cited alongside, same era.
Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. ICLR (2022)
2022
Cited alongside, same era.
LAION-AI: aesthetic-predictor. https://github.com/LAION-AI/aesthetic-predictor (2022)
2022
Cited alongside, same era.
Meng, C., He, Y., Song, Y., Song, J., Wu, J., Zhu, J.Y., Ermon, S.: Sdedit: Guided image synthesis and editing with stochastic differential equations. ICLR (2022)
2022
Cited alongside, same era.
2023
Later among the works it cites.
2023
Later among the works it cites.
Peebles, W., Xie, S.: Scalable diffusion models with transformers. ICCV (2023)
2023
Later among the works it cites.
Ruiz, N., Li, Y., Jampani, V., Pritch, Y., Rubinstein, M., Aberman, K.: Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation. CVPR (2023)
2023
Later among the works it cites.
Song, Y., Dhariwal, P., Chen, M., Sutskever, I.: Consistency models. ICML (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
Zhang, L., Rao, A., Agrawala, M.: Adding conditional control to text-to-image diffusion models. ICCV (2023)
2023
Later among the works it cites.
Chen, J., Huang, Y., Lv, T., Cui, L., Chen, Q., Wei, F.: Textdiffuser: Diffusion models as text painters. NeurIPS (2024)
2024
Closest in time.
Chen, J., Yu, J., Ge, C., Yao, L., Xie, E., Wu, Y., Wang, Z., Kwok, J., Luo, P., Lu, H., et al.: Pixart- α \alpha : Fast training of diffusion transformer for photorealistic text-to-image synthesis. ICLR (2024)
2024
Closest in time.
Guo, Y., Yang, C., Rao, A., Liang, Z., Wang, Y., Qiao, Y., Agrawala, M., Lin, D., Dai, B.: Animatediff: Animate your personalized text-to-image diffusion models without specific tuning. ICLR (2024)
2024
Closest in time.
Sun, K., Pan, J., Ge, Y., Li, H., Duan, H., Wu, X., Zhang, R., Zhou, A., Qin, Z., Wang, Y., et al.: Journeydb: A benchmark for generative image understanding. NeurIPS (2024)
2024
Closest in time.
Xue, Z., Song, G., Guo, Q., Liu, B., Zong, Z., Liu, Y., Luo, P.: Raphael: Text-to-image generation via large mixture of diffusion paths. NeurIPS (2024)
2024
Closest in time.
Zhao, S., Chen, D., Chen, Y.C., Bao, J., Hao, S., Yuan, L., Wong, K.Y.K.: Uni-controlnet: All-in-one control to text-to-image diffusion models. NeurIPS (2024)
2024
Closest in time.