Text-to-Image (T2I) models excel at synthesizing concepts such as nouns, appearances, and styles. To enable customized content creation based on a few example images of a concept, methods such as Textual Inversion and DreamBooth invert the desired concept and enable synthesizing it in new scenes. However, inverting personalized concepts that go beyond object appearance and style (adjectives and verbs) through natural language remains a challenge. Two key characteristics of these concepts contribute to the limitations of current inversion methods. 1) Adjectives and verbs are entangled with nouns (subject) and can hinder appearance-based inversion methods, where the subject appearance leaks into the concept embedding, and 2) describing such concepts often extends beyond single word embeddings. In this study, we introduce Lego, a textual inversion method designed to invert subject-entangled concepts from a few example images. Lego disentangles concepts from their associated subjects using a simple yet effective Subject Separation step and employs a Context Loss that guides the inversion of single/multi-embedding concepts. In a thorough user study, Lego-generated concepts were preferred over 70% of the time when compared to the baseline in terms of authentically generating concepts according to a reference. Additionally, visual question answering using an LLM suggested Lego-generated concepts are better aligned with the text description of the concept.

翻译：文本到图像（T2I）模型在合成名词、外观和风格等概念方面表现出色。为了基于概念的少量示例图像实现定制化内容生成，诸如Textual Inversion和DreamBooth等方法通过反演目标概念，使其能够在新的场景中被合成。然而，通过自然语言反演超越物体外观与风格（形容词和动词）的个性化概念仍具挑战性。这些概念的两个关键特性导致了当前反演方法的局限性：1）形容词和动词与名词（主体）相互纠缠，可能干扰基于外观的反演方法，导致主体外观泄露至概念嵌入中；2）描述此类概念通常需要超越单个词嵌入的表达。在本研究中，我们提出Lego，一种专为从少量示例图像中反演与主体纠缠的概念而设计的文本反演方法。Lego通过一个简单而有效的主体分离步骤将概念与其关联主体解耦，并采用上下文损失函数指导单/多嵌入概念的反演。在一项细致的用户研究中，与基线方法相比，Lego生成的概念在根据参考真实生成概念方面获得了超过70%的偏好度。此外，使用大型语言模型进行的视觉问答表明，Lego生成的概念与概念的文字描述具有更好的一致性。