Concept Conductor: Orchestrating Multiple Personalized Concepts in Text-to-Image Synthesis

Understand

The customization of text-to-image models has seen significant advancements, yet generating multiple personalized concepts remains a challenging task.

• Current methods struggle with attribute leakage and layout confusion when handling multiple concepts, leading to reduced concept fidelity and semantic consistency.
• In this work, we introduce a novel training-free framework, Concept Conductor, designed to ensure visual fidelity and correct layout in multi-concept customization.
• Concept Conductor isolates the sampling processes of multiple custom models to prevent attribute leakage between different concepts and corrects erroneous layouts through self-attention-based spatial guidance.