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cs.CV2026
UniCSG: Unified High-Fidelity Content-Constrained Style-Driven Generation via Staged Semantic and Frequency Disentanglement
Jingwei Yang, Ruoxi Wu, Wei Shen +4
Style transfer must match a target style while preserving content semantics. DiT-based diffusion models often suffer from content-style entanglement, leading to reference-content l…
cs.CV2024★ 1 cited
Foundation Cures Personalization: Improving Personalized Models' Prompt Consistency via Hidden Foundation Knowledge
Yiyang Cai, Zhengkai Jiang, Yulong Liu +4
Facial personalization faces challenges to maintain identity fidelity without disrupting the foundation model's prompt consistency. The mainstream personalization models employ ide…