Negative-Guided Subject Fidelity Optimization for Zero-Shot
  Subject-Driven Generation
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/
6840ee8b6b106ae42f553c3b
Chaehun Shin
claimed_verified
2025-06-05T07:27:16.951Z
631842124d24c00131a66656
/avatars/590633e163da425bf041e3988d8dd015.svg
shin
user
chaehun
6840ee8b6b106ae42f553c3c
Jooyoung Choi
claimed_verified
2025-06-05T07:27:19.172Z
62f9e54defd2de85492a4dde
/avatars/9925de0d02b2f2ed03cbafda44fd7de4.svg
Jooyoung Choi
user
jychoi
6840ee8b6b106ae42f553c3d
Johan Barthelemy
6840ee8b6b106ae42f553c3e
Jungbeom Lee
6840ee8b6b106ae42f553c3f
Sungroh Yoon
Negative-Guided Subject Fidelity Optimization for Zero-Shot
  Subject-Driven Generation
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/
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.
Subject Fidelity Optimization (SFO) comparative learning framework zero-shot subject-driven generation diffusion loss synthetic negative targets Condition-Degradation Negative Sampling (CDNS) diffusion timesteps