RealCustom: Narrowing Real Text Word for Real-Time Open-Domain
  Text-to-Image Customization
Mengqi Huang Zhendong Mao Mingcong Liu Qian He Yongdong Zhang
Text-to-image customization, which aims to synthesize text-driven images for
the given subjects, has recently revolutionized content creation. Existing
works follow the pseudo-word paradigm, i.e., represent the given subjects as
pseudo-words and then compose them with the given text. However, the inherent
entangled influence scope of pseudo-words with the given text results in a
dual-optimum paradox, i.e., the similarity of the given subjects and the
controllability of the given text could not be optimal simultaneously. We
present RealCustom that, for the first time, disentangles similarity from
controllability by precisely limiting subject influence to relevant parts only,
achieved by gradually narrowing real text word from its general connotation to
the specific subject and using its cross-attention to distinguish relevance.
Specifically, RealCustom introduces a novel "train-inference" decoupled
framework: (1) during training, RealCustom learns general alignment between
visual conditions to original textual conditions by a novel adaptive scoring
module to adaptively modulate influence quantity; (2) during inference, a novel
adaptive mask guidance strategy is proposed to iteratively update the influence
scope and influence quantity of the given subjects to gradually narrow the
generation of the real text word. Comprehensive experiments demonstrate the
superior real-time customization ability of RealCustom in the open domain,
achieving both unprecedented similarity of the given subjects and
controllability of the given text for the first time. The project page is
https://corleone-huang.github.io/realcustom/.