The artistic style within a painting is the means of expression, which includes not only the painting material, colors, and brushstrokes, but also the high-level attributes including semantic elements, object shapes, etc. Previous arbitrary example-guided artistic image generation methods often fail to control shape changes or convey elements. The pre-trained text-to-image synthesis diffusion probabilistic models have achieved remarkable quality, but it often requires extensive textual descriptions to accurately portray attributes of a particular painting. We believe that the uniqueness of an artwork lies precisely in the fact that it cannot be adequately explained with normal language. Our key idea is to learn artistic style directly from a single painting and then guide the synthesis without providing complex textual descriptions. Specifically, we assume style as a learnable textual description of a painting. We propose an inversion-based style transfer method (InST), which can efficiently and accurately learn the key information of an image, thus capturing and transferring the artistic style of a painting. We demonstrate the quality and efficiency of our method on numerous paintings of various artists and styles. Code and models are available at https://github.com/zyxElsa/InST.

翻译：绘画中的艺术风格是表达手段，不仅包含绘画材料、色彩和笔触，还涉及语义元素、物体形状等高层属性。以往的任意示例引导艺术图像生成方法往往难以控制形状变化或传递元素。预训练的文本到图像合成扩散概率模型虽已取得显著质量，但常需大量文字描述才能准确刻画特定画作的属性。我们相信艺术作品的独特性恰恰在于其难以用常规语言充分解释。我们的核心思想是直接从单幅画作中学习艺术风格，进而无需复杂文字描述即可引导图像合成。具体而言，我们将风格视为画作的可学习文本描述，提出一种基于逆映射的风格迁移方法（InST），可高效准确地提取图像关键信息，从而捕获并传递画作的艺术风格。我们通过大量不同艺术家与风格的画作验证了该方法的性能与效率。代码和模型已开源至 https://github.com/zyxElsa/InST。