LatexBlend：基于潜在文本混合的可扩展多概念定制生成
LatexBlend: Scaling Multi-concept Customized Generation with Latent Textual Blending
AI总结：
本文提出LaTexBlend框架，通过在潜在文本空间中对单个概念进行紧凑表示并自由混合，实现高效可扩展的多概念定制图像生成，显著提升生成质量和计算效率。
摘要：
定制化文本到图像生成能够根据文本提示将用户指定的概念渲染到新场景中。在定制生成中扩展概念数量可以满足更广泛的用户创作需求，但现有方法在生成质量和计算效率方面面临挑战。本文提出LaTexBlend，一种有效且高效地扩展多概念定制生成的新框架。LaTexBlend的核心思想是在潜在文本空间中表示单个概念并混合多个概念，该空间位于文本编码器和线性投影之后。LaTexBlend对每个概念单独进行定制，将其以紧凑的潜在文本特征表示存储在概念库中，该表示捕获了足够的概念信息以确保高保真度。在推理阶段，概念库中的概念可以在潜在文本空间中自由无缝地组合，为多概念生成提供两个关键优势：1）出色的可扩展性，以及2）显著减少去噪偏差，保持连贯的布局。大量实验表明，LaTexBlend能够灵活地整合多个定制概念，具有和谐的结构和高主体保真度，在生成质量和计算效率方面均大幅优于基线方法。我们的代码将公开发布。
英文摘要：
Customized text-to-image generation renders user-specified concepts into novel contexts based on textual prompts. Scaling the number of concepts in customized generation meets a broader demand for user creation, whereas existing methods face challenges with generation quality and computational efficiency. In this paper, we propose LaTexBlend, a novel framework for effectively and efficiently scaling multi-concept customized generation. The core idea of LaTexBlend is to represent single concepts and blend multiple concepts within a Latent Textual space, which is positioned after the text encoder and a linear projection. LaTexBlend customizes each concept individually, storing them in a concept bank with a compact representation of latent textual features that captures sufficient concept information to ensure high fidelity. At inference, concepts from the bank can be freely and seamlessly combined in the latent textual space, offering two key merits for multi-concept generation: 1) excellent scalability, and 2) significant reduction of denoising deviation, preserving coherent layouts. Extensive experiments demonstrate that LaTexBlend can flexibly integrate multiple customized concepts with harmonious structures and high subject fidelity, substantially outperforming baselines in both generation quality and computational efficiency. Our code will be publicly available.
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。
