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Customized text-to-image generation, which aims to learn user-specified concepts with a few images, has drawn significant attention recently.
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.
Attention is all you need
Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, Ł.; and Polosukhin, I. 2017 · 2017
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J.; Chang, M.-W.; Lee, K.; and Toutanova, K. 2018 · 2018
Earlier work this paper cites.
Decoupled Weight Decay Regularization
Loshchilov, I.; and Hutter, F. 2018 · 2018
Earlier work this paper cites.
Denoising Diffusion Probabilistic Models
Ho, J.; Jain, A.; and Abbeel, P. 2020 · 2020
Earlier work this paper cites.
Denoising Diffusion Implicit Models
Song, J.; Meng, C.; and Ermon, S. 2020 · 2020
Earlier work this paper cites.
Emerging Properties in Self-Supervised Vision Transformers
Caron, M.; Touvron, H.; Misra, I.; Jegou, H.; Mairal, J.; Bojanowski, P.; and Joulin, A. 2021 · 2021
Earlier work this paper cites.
Diffusion models beat gans on image synthesis
Dhariwal, P.; and Nichol, A. 2021 · 2021
Earlier work this paper cites.
LoRA: Low-Rank Adaptation of Large Language Models
Hu, E. J.; Wallis, P.; Allen-Zhu, Z.; Li, Y.; Wang, S.; Wang, L.; Chen, W.; et al. 2021 · 2021
Earlier work this paper cites.
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 · 2021
Earlier work this paper cites.
Laion-400m: Open dataset of clip-filtered 400 million image-text pairs
Schuhmann, C.; Vencu, R.; Beaumont, R.; Kaczmarczyk, R.; Mullis, C.; Katta, A.; Coombes, T.; Jitsev, J.; and Komatsuzaki, A. 2021 · 2021
Cited alongside, same era.
Blended Diffusion for Text-driven Editing of Natural Images
Avrahami, O.; Lischinski, D.; and Fried, O. 2022 · 2022
Cited alongside, same era.
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.
Classifier-free diffusion guidance
Ho, J.; and Salimans, T. 2022 · 2022
Cited alongside, same era.
Diffusionclip: Text-guided diffusion models for robust image manipulation
Kim, G.; Kwon, T.; and Ye, J. C. 2022 · 2022
Cited alongside, same era.
Break-A-Scene: Extracting Multiple Concepts from a Single Image
Avrahami, O.; Aberman, K.; Fried, O.; Cohen-Or, D.; and Lischinski, D. 2023 · 2023
Closest in time.
DisenBooth: Disentangled Parameter-Efficient Tuning for Subject-Driven Text-to-Image Generation
Chen, H.; Zhang, Y.; Wang, X.; Duan, X.; Zhou, Y.; and Zhu, W. 2023 · 2023
Closest in time.
Svdiff: Compact parameter space for diffusion fine-tuning
Han, L.; Li, Y.; Zhang, H.; Milanfar, P.; Metaxas, D.; and Yang, F. 2023 · 2023
Closest in time.
ViCo: Detail-Preserving Visual Condition for Personalized Text-to-Image Generation
Hao, S.; Han, K.; Zhao, S.; and Wong, K.-Y. K. 2023 · 2023
Closest in time.
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GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models
Nichol, A. Q.; Dhariwal, P.; Ramesh, A.; Shyam, P.; Mishkin, P.; Mcgrew, B.; Sutskever, I.; and Chen, M. 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.
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. 2023b
Cited in the paper.
Kirillov, A.; Mintun, E.; Ravi, N.; Mao, H.; Rolland, C.; Gustafson, L.; Xiao, T.; Whitehead, S.; Berg, A. C.; Lo, W.-Y.; et al. 2023 · 2023
Closest in time.
More Control for Free! Image Synthesis with Semantic Diffusion Guidance
Liu, X.; Park, D. H.; Azadi, S.; Zhang, G.; Chopikyan, A.; Hu, Y.; Shi, H.; Rohrbach, A.; and Darrell, T. 2023a · 2023
Closest in time.
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 · 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.
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.