ArtAdapter: Text-to-Image Style Transfer using Multi-Level Style Encoder and Explicit Adaptation

Dar-Yen Chen, Hamish Tennent, Ching-Wen Hsu
12/1/2023

No official code found. Best implementation: cardinalblue/ArtAdapter (34 stars, MIT, last commit 2023-12-07).

Abstract

This work introduces ArtAdapter, a transformative text-to-image (T2I) style transfer framework that transcends tra-ditional limitations of color, brushstrokes, and object shape, capturing high-level style elements such as composition and distinctive artistic expression. The integration of a multi-level style encoder with our proposed explicit adaptation mechanism enables ArtAdapter to achieve unprecedented fidelity in style transfer, ensuring close alignment with tex-tual descriptions. Additionally, the incorporation of an Aux-iliary Content Adapter (ACA) effectively separates content from style, alleviating the borrowing of content from style references. Moreover, our novel fast finetuning approach could further enhance zero-shot style representation while mitigating the risk of overfitting. Comprehensive evaluations confirm that ArtAdapter surpasses current state-of-the-art methods.

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Code Implementations(1)

cardinalblue/ArtAdapterCommunity70%
MIT

Text-to-Image Style Transfer using Multi-Level Style Encoder and Explicit Adaptation

340Dec 5, 20232 years agoMIT

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Cite this paper

@article{chen2023artadapter,
  title  = {ArtAdapter: Text-to-Image Style Transfer using Multi-Level Style Encoder and Explicit Adaptation},
  author = {Dar-Yen Chen and Hamish Tennent and Ching-Wen Hsu},
  year   = {2023},
  eprint = {2312.02109},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/pdf/2312.02109},
  journal = {CVPR 2023 2023}
}

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