ArtAdapter: Text-to-Image Style Transfer using Multi-Level Style Encoder and Explicit Adaptation
Dar-Yen Chen, Hamish Tennent, Ching-Wen Hsu
2024Year
14Top-tier citations
Abstract
A kitten sleeping on a pillow A river with rapids and rocks A bird in a wood A dog in the desert A snowy mountain peak A modern house with a pool A sailboat at sea A teacup on a saucer Figure 1. Our framework is capable of capturing faithful style representation, from low-level delicate texture to high-level minimalism composition, in either single or multiple style references, closely adhering to the textual prompts.
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Install the CLIlune papers fulltext 7d998788-df9e-4371-ac53-9d7f3548b693Cited by top-tier papers14
- SplitFlux: Learning to Decouple Content and Style from a Single ImageYitong Yang, Yinglin Wang, Changshuo Wang, Yongjun Zhang et al.CVPR 2026 · 5 citations
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- Co-Painter: Fine-Grained Controllable Image Stylization via Implicit Decoupling and Adaptive InjectionBowen Fu, Wei Wei, Jiaqi Tang, Jiangtao Nie et al.ICCV 2025 · 2 citations
Builds on29
- Learning Transferable Visual Models From Natural Language SupervisionAlec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh et al.ICML 2021 · 47,906 citations
- Denoising Diffusion Probabilistic ModelsJonathan Ho, Ajay Jain, Pieter AbbeelNeurIPS 2020 · 35,902 citations
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- LoRA: Low-Rank Adaptation of Large Language ModelsEdward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu et al.ICLR 2022 · 18,833 citations
- Diffusion Models Beat GANs on Image SynthesisPrafulla Dhariwal, Alexander Quinn NicholNeurIPS 2021 · 13,211 citations
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