RealCustom++: Representing Images as Real-Word for Real-Time Customization
<|reference_start|>RealCustom++: Representing Images as Real-Word for Real-Time Customization: Text-to-image customization, which takes given texts and images depicting given subjects as inputs, aims to synthesize new images that align with both text semantics and subject appearance. This task provides precise control over details that text alone cannot capture and is fundamental for various real-world applications, garnering significant interest from academia and industry. Existing works follow the pseudo-word paradigm, which involves representing given subjects as pseudo-words and combining them with given texts to collectively guide the generation. However, the inherent conflict and entanglement between the pseudo-words and texts result in a dual-optimum paradox, where subject similarity and text controllability cannot be optimal simultaneously. We propose a novel real-words paradigm termed RealCustom++ that instead represents subjects as non-conflict real words, thereby disentangling subject similarity from text controllability and allowing both to be optimized simultaneously. Specifically, RealCustom++ introduces a novel "train-inference" decoupled framework: (1) During training, RealCustom++ learns the alignment between vision conditions and all real words in the text, ensuring high subject-similarity generation in open domains. This is achieved by the cross-layer cross-scale projector to robustly and finely extract subject features, and a curriculum training recipe that adapts the generated subject to diverse poses and sizes. (2) During inference, leveraging the learned general alignment, an adaptive mask guidance is proposed to only customize the generation of the specific target real word, keeping other subject-irrelevant regions uncontaminated to ensure high text-controllability in real-time.<|reference_end|>
@article{mao2024realcustom++:,
    title={RealCustom++: Representing Images as Real-Word for Real-Time
  Customization},
    author={Zhendong Mao, Mengqi Huang, Fei Ding, Mingcong Liu, Qian He, Xiaojun
  Chang, Yongdong Zhang},
    journal={arXiv preprint arXiv:2408.09744},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.09744},
    primaryClass={cs.CV}
}