ADAPT：基于注意力的自适应提示调度与稀有概念生成的插值正交补集
ADAPT: Attention Driven Adaptive Prompt Scheduling and InTerpolating Orthogonal Complements for Rare Concepts Generation
AI总结：
ADAPT框架通过确定性提示调度和语义对齐，提升稀有概念生成效果，无需额外训练，在RareBench基准上表现优异。
摘要：
在文本到图像合成中生成稀有组合概念仍是一个挑战，特别是对于训练数据中不常见的属性。尽管最近的方法如R2F通过利用LLM进行提示调度来解决这一挑战，但它们因语言模型的随机性和迭代文本嵌入切换的次优指导而存在固有方差。为了解决这些问题，我们提出了ADAPT框架，这是一个无需训练的框架，能够确定性地规划和语义对齐提示调度，提供一致的指导以增强稀有概念的组合生成。通过利用注意力分数和正交成分，ADAPT显著增强了在RareBench基准中稀有概念的组合生成能力，而无需额外训练或微调。通过全面的实验，我们证明ADAPT在RareBench上实现了优越的性能，并准确反映了稀有属性的语义信息，提供了对稀有组合生成的确定性和精确控制，而不会影响视觉完整性。
英文摘要：
Generating rare compositional concepts in text-to-image synthesis remains a challenge for diffusion models, particularly for attributes that are uncommon in the training data. While recent approaches, such as R2F, address this challenge by utilizing LLM for prompt scheduling, they suffer from inherent variance due to the randomness of language models and suboptimal guidance from iterative text embedding switching. To address these problems, we propose the ADAPT framework, a training-free framework that deterministically plans and semantically aligns prompt schedules, providing consistent guidance to enhance the composition of rare concepts. By leveraging attention scores and orthogonal components, ADAPT significantly enhances compositional generation of rare concepts in the RareBench benchmark without additional training or fine-tuning. Through comprehensive experiments, we demonstrate that ADAPT achieves superior performance in RareBench and accurately reflects the semantic information of rare attributes, providing deterministic and precise control over the generation of rare compositions without compromising visual integrity.
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。
