Abstract
Artistic style transfer aims to transfer the learned style onto an arbitrary content image. However, most existing style transfer methods can only render consistent artistic stylized images, making it difficult for users to get enough stylized images to enjoy. To solve this issue, we propose a novel artistic style transfer framework called DyArtbank, which can generate diverse and highly realistic artistic stylized images. Specifically, we introduce a Dynamic Style Prompt ArtBank (DSPA), a set of learnable parameters. It can learn and store the style information from the collection of artworks, dynamically guiding pre-trained stable diffusion to generate diverse and highly realistic artistic stylized images. DSPA can also generate random artistic image samples with the learned style information, providing a new idea for data augmentation. Besides, a Key Content Feature Prompt (KCFP) module is proposed to provide sufficient content prompts for pre-trained stable diffusion to preserve the detailed structure of the input content image. Extensive qualitative and quantitative experiments verify the effectiveness of our proposed method. Code is available: this https URL
Abstract (translated)
艺术风格转换旨在将学习到的风格转移到任意的内容图像上。然而，大多数现有的风格转移方法只能生成一致的艺术化效果图片，这使得用户难以获得足够多的美化后的图片以供享受。为了解决这个问题，我们提出了一种新的艺术风格转移框架——DyArtbank，它可以生成多样且高度逼真的艺术风格化图像。 具体来说，我们引入了一个动态风格提示艺术银行（Dynamic Style Prompt ArtBank, DSPA），这是一个可学习参数的集合，可以从艺术品收藏中学习并存储风格信息，并能够引导预训练的稳定扩散模型以生成多样化且高度真实的艺术风格化图像。此外，DSPA 还可以利用已学得的风格信息生成随机的艺术图片样本，为数据增强提供了新的思路。同时，我们还提出了一种关键内容特征提示（Key Content Feature Prompt, KCFP）模块来提供足够的内容提示给预训练的稳定扩散模型，以保留输入内容图像的详细结构。 大量的定性和定量实验验证了我们所提出方法的有效性。代码可从以下链接获取：[此 URL]
URL
https://arxiv.org/abs/2503.08392
