Fetching the paper…
Reading the bibliography…
Personalized text-to-image generation has emerged as a powerful and sought-after tool, empowering users to create customized images based on their specific concepts and prompts.
Fairface: Face attribute dataset for balanced race, gender, and age
Kärkkäinen, K.; and Joo, J. 2019 · 1908
Earlier work this paper cites.
Multi-concept customization of text-to-image diffusion
Kumari, N.; Zhang, B.; Zhang, R.; Shechtman, E.; and Zhu, J.-Y. 2023 · 1941
Earlier work this paper cites.
Denoising diffusion implicit models
Song, J.; Meng, C.; and Ermon, S. 2020 · 2010
Earlier work this paper cites.
U-net: Convolutional networks for biomedical image segmentation
Ronneberger, O.; Fischer, P.; and Brox, T. 2015 · 2015
Earlier work this paper cites.
Progressive growing of gans for improved quality, stability, and variation
Karras, T.; Aila, T.; Laine, S.; and Lehtinen, J. 2017 · 2017
Earlier work this paper cites.
Arcface: Additive angular margin loss for deep face recognition
Deng, J.; Guo, J.; Xue, N.; and Zafeiriou, S. 2019 · 2019
Earlier work this paper cites.
A style-based generator architecture for generative adversarial networks
Karras, T.; Laine, S.; and Aila, T. 2019 · 2019
Earlier work this paper cites.
Denoising diffusion probabilistic models
Ho, J.; Jain, A.; and Abbeel, P. 2020 · 2020
Earlier work this paper cites.
Ilvr: Conditioning method for denoising diffusion probabilistic models
Choi, J.; Kim, S.; Jeong, Y.; Gwon, Y.; and Yoon, S. 2021 · 2021
Earlier work this paper cites.
Cogview: Mastering text-to-image generation via transformers
Ding, M.; Yang, Z.; Hong, W.; Zheng, W.; Zhou, C.; Yin, D.; Lin, J.; Zou, X.; Shao, Z.; Yang, H.; et al. 2021 · 2021
Earlier work this paper cites.
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 · 2021
Earlier work this paper cites.
Glide: Towards photorealistic image generation and editing with text-guided diffusion models
Nichol, A.; Dhariwal, P.; Ramesh, A.; Shyam, P.; Mishkin, P.; McGrew, B.; Sutskever, I.; and Chen, M. 2021 · 2021
Cited alongside, same era.
Zero-shot text-to-image generation
Ramesh, A.; Pavlov, M.; Goh, G.; Gray, S.; Voss, C.; Radford, A.; Chen, M.; and Sutskever, I. 2021 · 2021
Cited alongside, same era.
Tedigan: Text-guided diverse face image generation and manipulation
Xia, W.; Yang, Y.; Xue, J.-H.; and Wu, B. 2021 · 2021
Cited alongside, same era.
Dreamartist: Towards controllable one-shot text-to-image generation via contrastive prompt-tuning
Dong, Z.; Wei, P.; and Lin, L. 2022 · 2022
Cited alongside, same era.
Make-a-scene: Scene-based text-to-image generation with human priors
Gafni, O.; Polyak, A.; Ashual, O.; Sheynin, S.; Parikh, D.; and Taigman, Y. 2022 · 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 · 2022
Later among the works it cites.
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 · 2023
Closest in time.
Subject-driven text-to-image generation via apprenticeship learning
Chen, W.; Hu, H.; Li, Y.; Rui, N.; Jia, X.; Chang, M.-W.; and Cohen, W. W. 2023 · 2023
Closest in time.
Scaling up gans for text-to-image synthesis
Kang, M.; Zhu, J.-Y.; Zhang, R.; Park, J.; Shechtman, E.; Paris, S.; and Park, T. 2023 · 2023
Closest in time.
Subject-Diffusion: Open Domain Personalized Text-to-Image Generation without Test-time Fine-tuning
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
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 · 2022
Cited alongside, same era.
Hierarchical text-conditional image generation with clip latents
Ramesh, A.; Dhariwal, P.; Nichol, A.; Chu, C.; and Chen, M. 2022 · 2022
Cited alongside, same era.
High-resolution image synthesis with latent diffusion models
Rombach, R.; Blattmann, A.; Lorenz, D.; Esser, P.; and Ommer, B. 2022 · 2022
Cited alongside, same era.
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 · 2022
Cited alongside, same era.
Dual diffusion implicit bridges for image-to-image translation
Su, X.; Song, J.; Meng, C.; and Ermon, S. 2022 · 2022
Cited alongside, same era.
Unifying Diffusion Models’ Latent Space, with Applications to CycleDiffusion and Guidance
Wu, C. H.; and De la Torre, F. 2022 · 2022
Cited alongside, same era.
Encoder-based Domain Tuning for Fast Personalization of Text-to-Image Models
Gal, R.; Arar, M.; Atzmon, Y.; Bermano, A. H.; Chechik, G.; and Cohen-Or, D. 2023a
Cited in the paper.
Ma, J.; Liang, J.; Chen, C.; and Lu, H. 2023 · 2023
Closest in time.
Instantbooth: Personalized text-to-image generation without test-time finetuning
Shi, J.; Xiong, W.; Lin, Z.; and Jung, H. J. 2023 · 2023
Closest in time.
StyleDrop: Text-to-Image Generation in Any Style
Sohn, K.; Ruiz, N.; Lee, K.; Chin, D. C.; Blok, I.; Chang, H.; Barber, J.; Jiang, L.; Entis, G.; Li, Y.; et al. 2023 · 2023
Closest in time.
Elite: Encoding visual concepts into textual embeddings for customized text-to-image generation
Wei, Y.; Zhang, Y.; Ji, Z.; Bai, J.; Zhang, L.; and Zuo, W. 2023 · 2023
Closest in time.
Inserting Anybody in Diffusion Models via Celeb Basis
Yuan, G.; Cun, X.; Zhang, Y.; Li, M.; Qi, C.; Wang, X.; Shan, Y.; and Zheng, H. 2023 · 2023
Closest in time.
Adding conditional control to text-to-image diffusion models
Zhang, L.; and Agrawala, M. 2023 · 2023
Closest in time.
Zhou, Y.; Zhang, R.; Sun, T.; and Xu, J. 2023 · 2023
Closest in time.