ICAS: IP Adapter and ControlNet-based Attention Structure for Multi-Subject Style Transfer Optimization

• URL: http://arxiv.org/abs/2504.13224v1
• Date: Thu, 17 Apr 2025 10:48:11 GMT
• Title: ICAS: IP Adapter and ControlNet-based Attention Structure for Multi-Subject Style Transfer Optimization
• Authors: Fuwei Liu,
• Abstract summary: ICAS is a novel framework for efficient and controllable multi-subject style transfer.<n>Our framework ensures faithful global layout preservation alongside accurate local style synthesis.<n>ICAS achieves superior performance in structure preservation, style consistency, and inference efficiency.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generating multi-subject stylized images remains a significant challenge due to the ambiguity in defining style attributes (e.g., color, texture, atmosphere, and structure) and the difficulty in consistently applying them across multiple subjects. Although recent diffusion-based text-to-image models have achieved remarkable progress, existing methods typically rely on computationally expensive inversion procedures or large-scale stylized datasets. Moreover, these methods often struggle with maintaining multi-subject semantic fidelity and are limited by high inference costs. To address these limitations, we propose ICAS (IP-Adapter and ControlNet-based Attention Structure), a novel framework for efficient and controllable multi-subject style transfer. Instead of full-model tuning, ICAS adaptively fine-tunes only the content injection branch of a pre-trained diffusion model, thereby preserving identity-specific semantics while enhancing style controllability. By combining IP-Adapter for adaptive style injection with ControlNet for structural conditioning, our framework ensures faithful global layout preservation alongside accurate local style synthesis. Furthermore, ICAS introduces a cyclic multi-subject content embedding mechanism, which enables effective style transfer under limited-data settings without the need for extensive stylized corpora. Extensive experiments show that ICAS achieves superior performance in structure preservation, style consistency, and inference efficiency, establishing a new paradigm for multi-subject style transfer in real-world applications.

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