title: Negative-Guided Subject Fidelity Optimization for Zero-Shot
    Subject-Driven Generation
summary: 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/
paper_authors._id: 6840ee8b6b106ae42f553c3b
paper_authors.name: Chaehun Shin
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-05T07:27:16.951Z
paper_authors._id: 631842124d24c00131a66656
paper_authors.avatarUrl: /avatars/590633e163da425bf041e3988d8dd015.svg
paper_authors.fullname: shin
paper_authors.type: user
paper_authors.user: chaehun
paper_authors._id: 6840ee8b6b106ae42f553c3c
paper_authors.name: Jooyoung Choi
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-05T07:27:19.172Z
paper_authors._id: 62f9e54defd2de85492a4dde
paper_authors.avatarUrl: /avatars/9925de0d02b2f2ed03cbafda44fd7de4.svg
paper_authors.fullname: Jooyoung Choi
paper_authors.type: user
paper_authors.user: jychoi
paper_authors._id: 6840ee8b6b106ae42f553c3d
paper_authors.name: Johan Barthelemy
paper_authors._id: 6840ee8b6b106ae42f553c3e
paper_authors.name: Jungbeom Lee
paper_authors._id: 6840ee8b6b106ae42f553c3f
paper_authors.name: Sungroh Yoon
paper_title: Negative-Guided Subject Fidelity Optimization for Zero-Shot
    Subject-Driven Generation
paper_summary: 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/
paper_ai_summary: Subject Fidelity Optimization (SFO) enhances zero-shot subject-driven generation by introducing synthetic negative samples and optimizing diffusion timesteps, outperforming existing methods in subject fidelity and text alignment.
paper_ai_keywords: Subject Fidelity Optimization (SFO) comparative learning framework zero-shot subject-driven generation diffusion loss synthetic negative targets Condition-Degradation Negative Sampling (CDNS) diffusion timesteps