Abstract
Artistic style transfer in generative models remains a significant challenge, as existing methods often introduce style only via model fine-tuning, additional adapters, or prompt engineering, all of which can be computationally expensive and may still entangle style with subject matter. In this paper, we introduce a training- and inference-light, interpretable method for representing and transferring artistic style. Our approach leverages an art-specific Sparse Autoencoder (SAE) on top of latent embeddings of generative image models. Trained on artistic data, our SAE learns an emergent, largely disentangled set of stylistic and compositional concepts, corresponding to style-related elements pertaining brushwork, texture, and color palette, as well as semantic and structural concepts. We call it LouvreSAE and use it to construct style profiles: compact, decomposable steering vectors that enable style transfer without any model updates or optimization. Unlike prior concept-based style transfer methods, our method requires no fine-tuning, no LoRA training, and no additional inference passes, enabling direct steering of artistic styles from only a few reference images. We validate our method on ArtBench10, achieving or surpassing existing methods on style evaluations (VGG Style Loss and CLIP Score Style) while being 1.7-20x faster and, critically, interpretable.
Abstract (translated)
在生成模型中进行艺术风格转换仍然是一项重要挑战，现有方法通常通过微调模型、添加适配器或提示工程等方式引入风格，这些方式可能会消耗大量计算资源，并且仍可能导致风格与主题内容相互纠缠。在这篇论文中，我们提出了一种轻量级的训练和推理方法，用于表示并传输艺术风格。我们的方法利用了一个特定于艺术领域的稀疏自动编码器（SAE），该编码器基于生成图像模型的潜在嵌入之上构建。通过使用艺术数据进行训练，我们的SAE学习到了一组具有高度独立性的风格化与构图概念，这些概念对应着与笔触、纹理和色彩调板相关的元素以及语义和结构的概念。我们将这种方法命名为LouvreSAE，并利用它来构造风格配置文件：即紧凑且可分解的导向向量，这使得无需对模型进行任何更新或优化就能实现风格转换。 不同于之前的基于概念的方法，我们的方法不需要微调、LoRA训练或者额外的推理步骤，可以直接从少量参考图像中引导艺术风格。我们在ArtBench10数据集上验证了该方法的有效性，在评估风格时（包括VGG Style Loss和CLIP Score Style）达到了或超过了现有方法的表现水平，并且我们的方法速度提高了1.7到20倍，最为关键的是它是可解释的。
URL
https://arxiv.org/abs/2512.18930
