Abstract
Text-guided image generation has advanced rapidly with large-scale diffusion models, yet achieving precise stylization with visual exemplars remains difficult. Existing approaches often depend on task-specific retraining or expensive inversion procedures, which can compromise content integrity, reduce style fidelity, and lead to an unsatisfactory trade-off between semantic prompt adherence and style alignment. In this work, we introduce a training-free framework that reformulates style-guided synthesis as an in-context learning task. Guided by textual semantic prompts, our method concatenates a reference style image with a masked target image, leveraging a pretrained ReFlow-based inpainting model to seamlessly integrate semantic content with the desired style through multimodal attention fusion. We further analyze the imbalance and noise sensitivity inherent in multimodal attention fusion and propose a Dynamic Semantic-Style Integration (DSSI) mechanism that reweights attention between textual semantic and style visual tokens, effectively resolving guidance conflicts and enhancing output coherence. Experiments show that our approach achieves high-fidelity stylization with superior semantic-style balance and visual quality, offering a simple yet powerful alternative to complex, artifact-prone prior methods.
Abstract (translated)
基于文本的图像生成技术随着大规模扩散模型的发展而迅速进步，但利用视觉样本实现精确风格化仍然面临挑战。现有方法通常依赖于特定任务的重新训练或昂贵的逆向工程过程，这可能会损害内容完整性、降低风格保真度，并导致语义提示遵从性和风格对齐之间的不理想权衡。在这项工作中，我们引入了一个无需训练的框架，将基于样式的合成重构为一种上下文学习任务。通过文本语义提示引导，我们的方法结合了参考风格图像和掩码目标图像，利用预训练的ReFlow基底修复模型来无缝融合语义内容与所需样式，借助多模态注意力融合技术实现这一目标。 我们进一步分析了多模态注意力融合内在的不平衡性和噪声敏感性，并提出了一种动态语义-风格集成（DSSI）机制，通过重新加权文本语义和风格视觉标记之间的注意来有效解决指导冲突并增强输出的一致性。实验表明，我们的方法能够实现高保真度的风格化，具有卓越的语义-风格平衡和视觉质量，并为复杂且易产生伪影的方法提供了一个简单而强大的替代方案。
URL
https://arxiv.org/abs/2601.06605
