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Personalizing text-to-image models using a limited set of images for a specific object has been explored in subject-specific image generation.
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Langley, P · 2000
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U-net: Convolutional networks for biomedical image segmentation
Ronneberger, O., Fischer, P., and Brox, T · 2015
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Decoupled weight decay regularization
Loshchilov, I. and Hutter, F · 2019
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Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
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Emerging properties in self-supervised vision transformers
Caron, M., Touvron, H., Misra, I., Jégou, H., Mairal, J., Bojanowski, P., and Joulin, A · 2021
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Lora: Low-rank adaptation of large language models
Hu, E. J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., and Chen, W · 2021
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Learning transferable visual models from natural language supervision
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al · 2021
Earlier work this paper cites.
An image is worth one word: Personalizing text-to-image generation using textual inversion
Gal, R., Alaluf, Y., Atzmon, Y., Patashnik, O., Bermano, A. H., Chechik, G., and Cohen-Or, D · 2022
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Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation
Li, J., Li, D., Xiong, C., and Hoi, S · 2022
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Hierarchical text-conditional image generation with clip latents
Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., and Chen, M · 2022
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High-resolution image synthesis with latent diffusion models
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B · 2022
Earlier work this paper cites.
Photorealistic text-to-image diffusion models with deep language understanding
Saharia, C., Chan, W., Saxena, S., Li, L., Whang, J., Denton, E. L., Ghasemipour, K., Gontijo Lopes, R., Karagol Ayan, B., Salimans, T., et al · 2022
Cited alongside, same era.
Scaling autoregressive models for content-rich text-to-image generation
Yu, J., Xu, Y., Koh, J. Y., Luong, T., Baid, G., Wang, Z., Vasudevan, V., Ku, A., Yang, Y., Ayan, B. K., et al · 2022
Cited alongside, same era.
An image is worth multiple words: Multi-attribute inversion for constrained text-to-image synthesis
Agarwal, A., Karanam, S., Shukla, T., and Srinivasan, B. V · 2023
Cited alongside, same era.
A neural space-time representation for text-to-image personalization
Alaluf, Y., Richardson, E., Metzer, G., and Cohen-Or, D · 2023
Cited alongside, same era.
Aligning text-to-image models using human feedback
Lee, K., Liu, H., Ryu, M., Watkins, O., Du, Y., Boutilier, C., Abbeel, P., Ghavamzadeh, M., and Gu, S. S · 2023
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Cones: Concept neurons in diffusion models for customized generation
Liu, Z., Feng, R., Zhu, K., Zhang, Y., Zheng, K., Liu, Y., Zhao, D., Zhou, J., and Cao, Y · 2023
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Aligning text-to-image diffusion models with reward backpropagation
Prabhudesai, M., Goyal, A., Pathak, D., and Fragkiadaki, K · 2023
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Controlling text-to-image diffusion by orthogonal finetuning
Qiu, Z., Liu, W., Feng, H., Xue, Y., Feng, Y., Liu, Z., Zhang, D., Weller, A., and Schölkopf, B · 2023
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Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation
Ruiz, N., Li, Y., Jampani, V., Pritch, Y., Rubinstein, M., and Aberman, K · 2023
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Black, K., Janner, M., Du, Y., Kostrikov, I., and Levine, S · 2023
Cited alongside, same era.
Muse: Text-to-image generation via masked generative transformers
Chang, H., Zhang, H., Barber, J., Maschinot, A., Lezama, J., Jiang, L., Yang, M.-H., Murphy, K., Freeman, W. T., Rubinstein, M., et al · 2023
Cited alongside, same era.
Directly fine-tuning diffusion models on differentiable rewards
Clark, K., Vicol, P., Swersky, K., and Fleet, D. J · 2023
Cited alongside, same era.
Dpok: Reinforcement learning for fine-tuning text-to-image diffusion models
Fan, Y., Watkins, O., Du, Y., Liu, H., Ryu, M., Boutilier, C., Abbeel, P., Ghavamzadeh, M., Lee, K., and Lee, K · 2023
Cited alongside, same era.
Svdiff: Compact parameter space for diffusion fine-tuning
Han, L., Li, Y., Zhang, H., Milanfar, P., Metaxas, D., and Yang, F · 2023
Cited alongside, same era.
Pick-a-pic: An open dataset of user preferences for text-to-image generation
Kirstain, Y., Polyak, A., Singer, U., Matiana, S., Penna, J., and Levy, O · 2023
Cited alongside, same era.
Multi-concept customization of text-to-image diffusion
Kumari, N., Zhang, B., Zhang, R., Shechtman, E., and Zhu, J.-Y · 2023
Cited alongside, same era.
Closest in time.
Key-locked rank one editing for text-to-image personalization
Tewel, Y., Gal, R., Chechik, G., and Atzmon, Y · 2023
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p + p+ : Extended textual conditioning in text-to-image generation
Voynov, A., Chu, Q., Cohen-Or, D., and Aberman, K · 2023
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Human preference score: Better aligning text-to-image models with human preference
Wu, X., Sun, K., Zhu, F., Zhao, R., and Li, H · 2023
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Imagereward: Learning and evaluating human preferences for text-to-image generation
Xu, J., Liu, X., Wu, Y., Tong, Y., Li, Q., Ding, M., Tang, J., and Dong, Y · 2023
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Prospect: Expanded conditioning for the personalization of attribute-aware image generation
Zhang, Y., Dong, W., Tang, F., Huang, N., Huang, H., Ma, C., Lee, T.-Y., Deussen, O., and Xu, C · 2023
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