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 introduces a novel 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 representations as implicit guidance and a trainable flow model producing the original conditional prediction, jointly achieving pure learning for personalized concepts. Furthermore, PureCC introduces a novel adaptive guidance scale λ^star to dynamically adjust the guidance strength of the target concept, balancing 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. The code is available at https://github.com/lzc-sg/PureCC.
69af80d1b3ab31dee1281c11
Zhichao Liao
claimed_verified
2026-03-10T09:45:10.726Z
66eb9d5d7f17088991ecbae2
https://cdn-avatars.huggingface.co/v1/production/uploads/66eb9d5d7f17088991ecbae2/khI2KZ4ZGJ891iOf-O0BF.jpeg
Zhichao Liao
ChaosLiao
user
ChaosLiao
69af80d1b3ab31dee1281c12
Xiaole Xian
69af80d1b3ab31dee1281c13
Qingyu Li
69af80d1b3ab31dee1281c14
Wenyu Qin
69af80d1b3ab31dee1281c15
Meng Wang
69af80d1b3ab31dee1281c16
Weicheng Xie
69af80d1b3ab31dee1281c17
Siyang Song
69af80d1b3ab31dee1281c18
Pingfa Feng
69af80d1b3ab31dee1281c19
Long Zeng
69af80d1b3ab31dee1281c1a
Liang Pan
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 introduces a novel 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 representations as implicit guidance and a trainable flow model producing the original conditional prediction, jointly achieving pure learning for personalized concepts. Furthermore, PureCC introduces a novel adaptive guidance scale λ^star to dynamically adjust the guidance strength of the target concept, balancing 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. The code is available at https://github.com/lzc-sg/PureCC.
PureCC presents a concept customization method that preserves original model behavior through decoupled learning and adaptive guidance scaling.
concept customization decoupled learning objective implicit guidance conditional prediction dual-branch training pipeline frozen extractor trainable flow model adaptive guidance scale model preservation