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arXiv · 2602.21402

FlowFixer: Towards Detail-Preserving Subject-Driven Generation

Jinyoung Jun·Won-Dong Jang·Wenbin Ouyang·Raghudeep Gadde·Jungbeom Lee
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Abstract

We present FlowFixer, a refinement framework for subject-driven generation (SDG) that restores fine details lost during generation caused by changes in scale and perspective of a subject. FlowFixer proposes direct image-to-image translation from visual references, avoiding ambiguities in language prompts. To enable image-to-image training, we introduce a one-step denoising scheme to generate self-supervised training data, which automatically removes high-frequency details while preserving global structure, effectively simulating real-world SDG errors. We further propose a keypoint matching-based metric to properly assess fidelity in details beyond semantic similarities usually measured by CLIP or DINO. Experimental results demonstrate that FlowFixer outperforms state-of-the-art SDG methods in both qualitative and quantitative evaluations, setting a new benchmark for high-fidelity subject-driven generation.

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Jinyoung Jun, Won-Dong Jang, Wenbin Ouyang, Raghudeep Gadde, Jungbeom Lee. 2026-02-24. FlowFixer: Towards Detail-Preserving Subject-Driven Generation. https://arxiv.org/abs/2602.21402

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