LogoSticker：将徽标插入扩散模型以实现定制化生成
LogoSticker: Inserting Logos into Diffusion Models for Customized Generation
AI总结：
本文提出LogoSticker，一种两阶段流水线，通过演员-评论家关系预训练和解耦身份学习，将徽标插入扩散模型，实现多样化情境下的准确和谐生成。
摘要：
近年来，文本到图像模型定制化的进展强调了通过少量示例整合新概念的重要性。然而，这些进展在很大程度上局限于广泛认可的主题，这些主题可以通过模型充足的共享先验知识相对容易地学习。相比之下，徽标以其独特的图案和文本元素为特征，难以在扩散模型中建立共享知识，因此构成了独特的挑战。为了弥合这一差距，我们引入了徽标插入任务。我们的目标是将徽标身份插入扩散模型，并使其能够在各种情境中无缝合成。我们提出了一种新颖的两阶段流水线LogoSticker来处理此任务。首先，我们提出了演员-评论家关系预训练算法，该算法解决了模型对徽标潜在空间定位及与其他对象交互的理解方面的非平凡差距。其次，我们提出了一种解耦的身份学习算法，该算法能够实现徽标的精确定位和身份提取。LogoSticker能够在多样化的情境中准确且和谐地生成徽标。我们全面验证了LogoSticker相对于定制化方法和大型模型（如DALLE~3）的有效性。项目页面：https://mingkangz.github.io/logosticker
英文摘要：
Recent advances in text-to-image model customization have underscored the importance of integrating new concepts with a few examples. Yet, these progresses are largely confined to widely recognized subjects, which can be learned with relative ease through models' adequate shared prior knowledge. In contrast, logos, characterized by unique patterns and textual elements, are hard to establish shared knowledge within diffusion models, thus presenting a unique challenge. To bridge this gap, we introduce the task of logo insertion. Our goal is to insert logo identities into diffusion models and enable their seamless synthesis in varied contexts. We present a novel two-phase pipeline LogoSticker to tackle this task. First, we propose the actor-critic relation pre-training algorithm, which addresses the nontrivial gaps in models' understanding of the potential spatial positioning of logos and interactions with other objects. Second, we propose a decoupled identity learning algorithm, which enables precise localization and identity extraction of logos. LogoSticker can generate logos accurately and harmoniously in diverse contexts. We comprehensively validate the effectiveness of LogoSticker over customization methods and large models such as DALLE~3. \href{https://mingkangz.github.io/logosticker}{Project page}.
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。
