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In this paper, we show that, a good style representation is crucial and sufficient for generalized style transfer without test-time tuning.
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Xun Huang, Ming-Yu Liu, Serge Belongie, and Jan Kautz · 2018
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Generative adversarial networks
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Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Adaattn: Revisit attention mechanism in arbitrary neural style transfer
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Alex Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever, and Mark Chen · 2021
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Style aligned image generation via shared attention
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Training-free style transfer emerges from h-space in diffusion models
Jaeseok Jeong, Mingi Kwon, and Youngjung Uh · 2023
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Stylecrafter: Enhancing stylized text-to-video generation with style adapter
Gongye Liu, Menghan Xia, Yong Zhang, Haoxin Chen, Jinbo Xing, Xintao Wang, Yujiu Yang, and Ying Shan · 2023
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Specialist diffusion: Plug-and-play sample-efficient fine-tuning of text-to-image diffusion models to learn any unseen style
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Simple disentanglement of style and content in visual representations
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Zhengwentai Sun, Yanghong Zhou, Honghong He, and PY Mok · 2023
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Zero-shot contrastive loss for text-guided diffusion image style transfer
Serin Yang, Hyunmin Hwang, and Jong Chul Ye · 2023
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Ip-adapter: Text compatible image prompt adapter for text-to-image diffusion models
Hu Ye, Jun Zhang, Sibo Liu, Xiao Han, and Wei Yang · 2023
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Inversion-based style transfer with diffusion models
Yuxin Zhang, Nisha Huang, Fan Tang, Haibin Huang, Chongyang Ma, Weiming Dong, and Changsheng Xu · 2023
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Style injection in diffusion: A training-free approach for adapting large-scale diffusion models for style transfer
Jiwoo Chung, Sangeek Hyun, and Jae-Pil Heo · 2024
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Styledrop: Text-to-image synthesis of any style
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