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Recent advances in text-to-image model customization have underscored the importance of integrating new concepts with a few examples.
2015
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2015
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Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. In: 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 21
2020
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Caron, M., Touvron, H., Misra, I., Jégou, H., Mairal, J., Bojanowski, P., Joulin, A.: Emerging properties in self-supervised vision transformers. In: ICCV (2021)
2021
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Goh, G., Cammarata, N., Voss, C., Carter, S., Petrov, M., Schubert, L., Radford, A., Olah, C.: Multimodal neurons in artificial neural networks. Distill (2021)
2021
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Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., Sutskever, I.: Learning transferable visual models from natural language supervision. In: ICML (2021)
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2021
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Song, J., Meng, C., Ermon, S.: Denoising diffusion implicit models. In: ICLR (2021)
2021
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Song, Y., Sohl-Dickstein, J., Kingma, D.P., Kumar, A., Ermon, S., Poole, B.: Score-based generative modeling through stochastic differential equations. In: ICLR (2021)
2021
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2022
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2022
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2022
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von Platen, P., Patil, S., Lozhkov, A., Cuenca, P., Lambert, N., Rasul, K., Davaadorj, M., Wolf, T.: Diffusers: State-of-the-art diffusion models. https://github.com/huggingface/diffusers (2022)
2022
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2022
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2023
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2023
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Kumari, N., Zhang, B., Zhang, R., Shechtman, E., Zhu, J.Y.: Multi-concept customization of text-to-image diffusion. In: CVPR (2023)
2023
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2023
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Saharia, C., Chan, W., Saxena, S., Li, L., Whang, J., Denton, E.L., Ghasemipour, K., Gontijo Lopes, R., Karagol Ayan, B., Salimans, T., Ho, J., Fleet, D.J., Norouzi, M.: Photorealistic text-to-image diffusion models with deep language understanding. In: NeurIPS (2022)
2022
Cited alongside, same era.
2023
Cited alongside, same era.
Betker, J., Goh, G., Jing, L., Brooks, T., Wang, J., Li, L., Ouyang, L., Zhuang, J., Lee, J., Guo, Y., Manassra, W., Dhariwal, P., Chu, C., Jiao, Y., Ramesh, A.: Improving image generation with better captions. https://openai.com/dall-e-3 (2023)
2023
Cited alongside, same era.
2023
Cited alongside, same era.
Chen, J., Huang, Y., Lv, T., Cui, L., Chen, Q., Wei, F.: Textdiffuser: Diffusion models as text painters. In: NeurIPS (2023)
2023
Cited alongside, same era.
2023
Cited alongside, same era.
Gal, R., Alaluf, Y., Atzmon, Y., Patashnik, O., Bermano, A.H., Chechik, G., Cohen-or, D.: An image is worth one word: Personalizing text-to-image generation using textual inversion. In: ICLR (2023)
2023
Cited alongside, same era.
2023
Cited alongside, same era.
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. In: CVPR (2023)
2023
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Sarukkai, V., Li, L., Ma, A., Ré, C., Fatahalian, K.: Collage diffusion. arXiv:2303.00262 (2023)
2023
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2023
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Yang, Y., Gui, D., Yuan, Y., Liang, W., Ding, H., Hu, H., Chen, K.: Glyphcontrol: Glyph conditional controllable visual text generation. In: NeurIPS (2023)
2023
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2024
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