UniCSG: Unified High-Fidelity Content-Constrained Style-Driven Generation via Staged Semantic and Frequency Disentanglement

• URL: http://arxiv.org/abs/2604.17850v1
• Date: Mon, 20 Apr 2026 05:59:20 GMT
• Title: UniCSG: Unified High-Fidelity Content-Constrained Style-Driven Generation via Staged Semantic and Frequency Disentanglement
• Abstract summary: DiT-based diffusion models often suffer from content-style entanglement, leading to reference-content leakage and unstable generation.<n>We present UniCSG, a unified framework for content-constrained, style-driven generation in both text-guided and reference-guided settings.
• Score: 13.265643008768825
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 leakage and unstable generation. We present UniCSG, a unified framework for content-constrained, style-driven generation in both text-guided and reference-guided settings. UniCSG employs staged training: (i) a latent-space semantic disentanglement stage that combines low-frequency preprocessing with conditioning corruption to encourage content-style separation, and (ii) a latent-space frequency-aware detail reconstruction stage that refines details via multi-scale frequency supervision. We further incorporate pixel-space reward learning to align latent objectives with perceptual quality after decoding. Experiments demonstrate improved content faithfulness, style alignment, and robustness in both settings.

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