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<p>To address the restoration of artworks and enhance the conservation of uniquely styled paintings using computational techniques such as large-parameter models, we introduce a diffusion-based network, namely Imagest, a novel multi-conditional artwork synthesis method. Specifically designed to overcome the limitations of existing restoration techniques for heavily damaged artworks, Imagest addresses the challenges of insufficient conditioning and inconsistent synthesis results. By jointly leveraging stylistic image and text prompts, our method facilitates more accurate and stylistically coherent reconstructions. Experiments conducted on the WikiArt and Chinese traditional painting datasets demonstrate Imagest’s efficiency in generating artworks that align with given style and content prompts. Compared to state-of-the-art baselines such as DALL.E 2 and Stable Diffusion, Imagest achieves competitive performance in both image inpainting and text-guided synthesis, as evidenced by favorable FID and CLIP-score metrics.</p>

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Imagest: a style conditioned art painting synthesis and generation networks

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月28日

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  • Jinghao Hu,
  • Pengbo Zhou,
  • Jian Gao,
  • Yuhe Zhang,
  • Guohua Geng,
  • Mingquan Zhou

摘要

To address the restoration of artworks and enhance the conservation of uniquely styled paintings using computational techniques such as large-parameter models, we introduce a diffusion-based network, namely Imagest, a novel multi-conditional artwork synthesis method. Specifically designed to overcome the limitations of existing restoration techniques for heavily damaged artworks, Imagest addresses the challenges of insufficient conditioning and inconsistent synthesis results. By jointly leveraging stylistic image and text prompts, our method facilitates more accurate and stylistically coherent reconstructions. Experiments conducted on the WikiArt and Chinese traditional painting datasets demonstrate Imagest’s efficiency in generating artworks that align with given style and content prompts. Compared to state-of-the-art baselines such as DALL.E 2 and Stable Diffusion, Imagest achieves competitive performance in both image inpainting and text-guided synthesis, as evidenced by favorable FID and CLIP-score metrics.