AttenST：一种基于预训练扩散模型的免训练注意力驱动风格迁移框架
AttenST: A Training-Free Attention-Driven Style Transfer Framework with Pre-Trained Diffusion Models
AI总结：
AttenST提出免训练注意力驱动风格迁移框架，通过风格引导自注意力、风格保持反演、内容感知自适应实例归一化和双特征交叉注意力，在降低计算成本的同时实现结构保真与风格表达的最优融合。
摘要：
尽管扩散模型在风格迁移任务中取得了显著进展，但现有方法通常依赖在推理过程中微调或优化预训练模型，导致计算成本高昂，并且难以平衡内容保留与风格融合。为了解决这些局限性，我们提出了AttenST，一个免训练的注意力驱动风格迁移框架。具体来说，我们提出了一种风格引导的自注意力机制，通过保留内容图像的query，同时用风格图像的key和value替换其key和value，使自注意力以参考风格为条件，从而实现有效的风格特征融合。为了减轻反演过程中的风格信息损失，我们引入了一种风格保持反演策略，通过多次重采样步骤提高反演精度。此外，我们提出了一种内容感知的自适应实例归一化方法，将内容统计信息整合到归一化过程中，以优化风格融合，同时缓解内容退化。进一步地，我们引入了一种双特征交叉注意力机制来融合内容特征和风格特征，确保结构保真度与风格表达之间的和谐综合。大量实验表明，AttenST优于现有方法，在风格迁移数据集上取得了最先进的性能。
英文摘要：
While diffusion models have achieved remarkable progress in style transfer tasks, existing methods typically rely on fine-tuning or optimizing pre-trained models during inference, leading to high computational costs and challenges in balancing content preservation with style integration. To address these limitations, we introduce AttenST, a training-free attention-driven style transfer framework. Specifically, we propose a style-guided self-attention mechanism that conditions self-attention on the reference style by retaining the query of the content image while substituting its key and value with those from the style image, enabling effective style feature integration. To mitigate style information loss during inversion, we introduce a style-preserving inversion strategy that refines inversion accuracy through multiple resampling steps. Additionally, we propose a content-aware adaptive instance normalization, which integrates content statistics into the normalization process to optimize style fusion while mitigating the content degradation. Furthermore, we introduce a dual-feature cross-attention mechanism to fuse content and style features, ensuring a harmonious synthesis of structural fidelity and stylistic expression. Extensive experiments demonstrate that AttenST outperforms existing methods, achieving state-of-the-art performance in style transfer dataset.
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