DBFP: PLUG-AND-PLAY DUAL-BAND FUSION FIXED-POINT SAMPLING FOR SUBJECT-DRIVEN GENERATION

Subject-driven generation must preserve a reference subject’s identity while executing a text edit—two objectives that compete under a single sampling condition. Even strong backbones leave this tension unresolved: following the prompt more closely drifts identity, while clinging to the reference weakens instruction following. Evaluation compounds the difficulty by scoring the two axes separately, without a compact criterion for their joint balance. To this end, we present Dual-Band Fusion Fixed-Point (DBFP), a training-free plug-and-play sampler. Given only a reference image $I_c$ and an edit prompt $p_{ed}$, it builds a structure prompt that preserves subject attributes while absorbing salient edit keywords, pairs it with the original instruction, fuses the two guided velocities by early-stage frequency-band mixing in Fourier space, and refines the mixed step with a short Picard loop—without touching weights or embeddings. We further propose the Subject-Text Harmonic Score (STHS) as a compact measure of the coupled identity–edit balance. Experiments on multiple subject-driven generation backbones show that DBFP improves this balance and, under the same experimental protocol, yields stronger overall subject-driven generation performance as a dropin sampling plug-in.