Arbitrary Style Transfer (AST) aims to transform images by adopting the style from any selected artwork. Nonetheless, the need to accommodate diverse and subjective user preferences poses a significant challenge. While some users wish to preserve distinct content structures, others might favor a more pronounced stylization. Despite advances in feed-forward AST methods, their limited customizability hinders their practical application. We propose a new approach, ArtFusion, which provides a flexible balance between content and style. In contrast to traditional methods reliant on biased similarity losses, ArtFusion utilizes our innovative Dual Conditional Latent Diffusion Probabilistic Models (Dual-cLDM). This approach mitigates repetitive patterns and enhances subtle artistic aspects like brush strokes and genre-specific features. Despite the promising results of conditional diffusion probabilistic models (cDM) in various generative tasks, their introduction to style transfer is challenging due to the requirement for paired training data. ArtFusion successfully navigates this issue, offering more practical and controllable stylization. A key element of our approach involves using a single image for both content and style during model training, all the while maintaining effective stylization during inference. ArtFusion outperforms existing approaches on outstanding controllability and faithful presentation of artistic details, providing evidence of its superior style transfer capabilities. Furthermore, the Dual-cLDM utilized in ArtFusion carries the potential for a variety of complex multi-condition generative tasks, thus greatly broadening the impact of our research.

翻译：任意风格迁移（AST）旨在通过采纳任意选定艺术作品的风格来转换图像。然而，适应多样化且主观的用户偏好是一个重大挑战。部分用户希望保留清晰的内容结构，而另一些用户可能偏好更强烈的风格化效果。尽管前馈式AST方法取得了进展，但其有限的定制能力制约了实际应用。我们提出新方法ArtFusion，在内容与风格之间提供灵活的平衡。与依赖有偏相似性损失的传统方法不同，ArtFusion采用创新的双条件隐式扩散概率模型（Dual-cLDM）。该方法能够减少重复图案，并增强笔触和体裁特定特征等细腻艺术细节。尽管条件扩散概率模型（cDM）在多种生成任务中展现出令人期待的效果，但因其对配对训练数据的需求，将其引入风格迁移领域仍面临挑战。ArtFusion成功克服该问题，提供更实用且可控的风格化方案。本方法的核心在于模型训练时使用单一图像同时作为内容和风格来源，同时在推理阶段保持有效的风格化效果。ArtFusion在卓越的可控性和艺术细节忠实呈现方面超越现有方法，为更优的风格迁移能力提供了证据。此外，ArtFusion采用的Dual-cLDM具备应用于多种复杂多条件生成任务的潜力，从而显著拓展了本研究的学术影响力。