ArtFusion：使用双条件潜在扩散模型的可控任意风格迁移
ArtFusion: Controllable Arbitrary Style Transfer using Dual Conditional Latent Diffusion Models
AI总结：
ArtFusion提出双条件潜在扩散模型，实现可控任意风格迁移，平衡内容与风格，优于现有方法。
摘要：
任意风格迁移（Arbitrary Style Transfer, AST）旨在通过采用任意选定艺术作品的风格来转换图像。然而，满足多样化和主观的用户偏好的需求构成了重大挑战。虽然一些用户希望保留清晰的内容结构，但其他用户可能更倾向于更明显的风格化。尽管前馈式AST方法取得了进展，但其有限的可定制性阻碍了实际应用。我们提出了一种新方法ArtFusion，它在内容和风格之间提供了灵活的平衡。与依赖有偏相似性损失的传统方法不同，ArtFusion利用我们创新的双条件潜在扩散概率模型（Dual-cLDM）。该方法减轻了重复模式，并增强了诸如笔触和流派特定特征等微妙的艺术方面。尽管条件扩散概率模型（cDM）在各种生成任务中取得了有希望的结果，但由于需要成对的训练数据，将其引入风格迁移具有挑战性。ArtFusion成功解决了这一问题，提供了更实用和可控的风格化。我们方法的一个关键要素是在模型训练期间使用单个图像同时作为内容和风格，同时在推理期间保持有效的风格化。ArtFusion在出色的可控性和艺术细节的忠实呈现方面优于现有方法，证明了其卓越的风格迁移能力。此外，ArtFusion中使用的Dual-cLDM具有应用于各种复杂多条件生成任务的潜力，从而大大拓宽了我们研究的影响。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
