RealCustom: Narrowing Real Text Word for Real-Time Open-Domain Text-to-Image Customization

Understand

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.