PureCC: Pure Learning for Text-to-Image Concept Customization

Existing concept customization methods have achieved remarkable outcomes in high-fidelity and multi-concept customization.However, they often neglect the influence on the original model's behavior and capabilities when learning new personalized concepts.To address this issue, we propose PureCC. PureCC novelly introduces a decoupled learning objective for concept customization, which combines the implicit guidance of the target concept with the original conditional prediction. This separated form enables PureCC to substantially focus on the original model during training. Moreover, based on this objective, PureCC designs a dual-branch training pipeline that includes a frozen extractor providing purified target concept representation as implicit guidance and a trainable flow model producing the original conditional prediction, jointly achieving pure learning for personalized concept. Furthermore, PureCC introduces an novel adaptive guidance scale $\lambda^\star$ to dynamically adjust the guidance strength of the target concept, balancing between customization fidelity and model preservation. Extensive experiments show that PureCC achieves state-of-the-art performance in preserving the original behavior and capabilities while enabling high-fidelity concept customization.