Negative-Guided Subject Fidelity Optimization for Zero-Shot
  Subject-Driven Generation
Authors: name: Chaehun Shin, name: Jooyoung Choi, name: Johan Barthelemy, name: Jungbeom Lee, name: Sungroh Yoon

We present Subject Fidelity Optimization (SFO), a novel comparative learning
framework for zero-shot subject-driven generation that enhances subject
fidelity. Beyond supervised fine-tuning methods that rely only on positive
targets and use the diffusion loss as in the pre-training stage, SFO introduces
synthetic negative targets and explicitly guides the model to favor positives
over negatives through pairwise comparison. For negative targets, we propose
Condition-Degradation Negative Sampling (CDNS), which automatically generates
distinctive and informative negatives by intentionally degrading visual and
textual cues without expensive human annotations. Moreover, we reweight the
diffusion timesteps to focus finetuning on intermediate steps where subject
details emerge. Extensive experiments demonstrate that SFO with CDNS
significantly outperforms baselines in terms of both subject fidelity and text
alignment on a subject-driven generation benchmark. Project page:
https://subjectfidelityoptimization.github.io/