标题： 嵌套注意力：用于概念个性化的语义感知注意力值 显示英文标题

标题： Nested Attention: Semantic-aware Attention Values for Concept Personalization

摘要： 将文本到图像模型个性化以在各种场景和风格中生成特定主题的图像是一个快速发展的领域。 当前的方法常常面临在身份保留和与输入文本提示对齐之间保持平衡的挑战。 一些方法依赖于一个文本标记来表示一个主题，这限制了表达能力，而其他方法则采用更丰富的表示，但会破坏模型的先验知识，降低提示对齐度。 在本工作中，我们引入了嵌套注意力，这是一种新颖的机制，它将丰富且具有表现力的图像表示注入到模型现有的交叉注意力层中。 我们的核心思想是生成依赖于查询的主题值，这些值来自学习为生成图像中的每个区域选择相关主题特征的嵌套注意力层。 我们将这些嵌套层集成到基于编码器的个性化方法中，并表明它们能够在遵循输入文本提示的同时实现高身份保留。 我们的方法具有通用性，可以在各种领域进行训练。 此外，其先验保留特性使我们能够在一个图像中结合来自不同领域的多个个性化主题。 显示英文摘要

摘要： Personalizing text-to-image models to generate images of specific subjects across diverse scenes and styles is a rapidly advancing field. Current approaches often face challenges in maintaining a balance between identity preservation and alignment with the input text prompt. Some methods rely on a single textual token to represent a subject, which limits expressiveness, while others employ richer representations but disrupt the model's prior, diminishing prompt alignment. In this work, we introduce Nested Attention, a novel mechanism that injects a rich and expressive image representation into the model's existing cross-attention layers. Our key idea is to generate query-dependent subject values, derived from nested attention layers that learn to select relevant subject features for each region in the generated image. We integrate these nested layers into an encoder-based personalization method, and show that they enable high identity preservation while adhering to input text prompts. Our approach is general and can be trained on various domains. Additionally, its prior preservation allows us to combine multiple personalized subjects from different domains in a single image.