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Advanced diffusion-based Text-to-Image (T2I) models, such as the Stable Diffusion Model, have made significant progress in generating diverse and high-quality images using text prompts alone.
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2022
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N. Huang, F. Tang, W. Dong, and C. Xu, “Draw your art dream: Diverse digital art synthesis with multimodal guided diffusion,” in
2022
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2023
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Cited alongside, same era.
2022
Cited alongside, same era.
O. Avrahami, D. Lischinski, and O. Fried, “Blended diffusion for text-driven editing of natural images,” in
2022
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A. Hertz, R. Mokady, J. Tenenbaum, K. Aberman, Y. Pritch, and D. Cohen-or, “Prompt-to-prompt image editing with cross-attention control,” in
2022
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2022
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J. Ho and T. Salimans, “Classifier-free diffusion guidance,”
2022
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2022
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R. Gal, Y. Alaluf, Y. Atzmon, O. Patashnik, A. H. Bermano, G. Chechik, and D. Cohen-Or, “An image is worth one word: Personalizing text-to-image generation using textual inversion,”
2023
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2023
Later among the works it cites.
2023
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2023
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2023
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2023
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2023
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2023
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2023
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2023
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2023
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2023
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Z. Zuo, A. Li, Z. Wang, L. Zhao, J. Dong, X. Wang, and M. Wang, “Statistics enhancement generative adversarial networks for diverse conditional image synthesis,”
2024
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J. Sun, Q. Deng, Q. Li, M. Sun, Y. Liu, and Z. Sun, “Anyface++: A unified framework for free-style text-to-face synthesis and manipulation,”
2024
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2024
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H. Chen, Y. Zhang, X. Wang, X. Duan, Y. Zhou, and W. Zhu, “Disendreamer: Subject-driven text-to-image generation with sample-aware disentangled tuning,”
2024
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2024
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O. Patashnik, Z. Wu, E. Shechtman, D. Cohen-Or, and D. Lischinski, “Styleclip: Text-driven manipulation of stylegan imagery,” in
2094
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