  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. Additionally, we present a concept injection technique that employs
shape-aware masks to specify the generation area for each concept. This
technique injects the structure and appearance of personalized concepts through
feature fusion in the attention layers, ensuring harmony in the final image.
Extensive qualitative and quantitative experiments demonstrate that Concept
Conductor can consistently generate composite images with accurate layouts
while preserving the visual details of each concept. Compared to existing
baselines, Concept Conductor shows significant performance improvements. Our
method supports the combination of any number of concepts and maintains high
fidelity even when dealing with visually similar concepts. The code and models
are available at https://github.com/Nihukat/Concept-Conductor.
