标题： SPF-肖像：面向语义无污染微调的纯肖像定制 显示英文标题

标题： SPF-Portrait: Towards Pure Portrait Customization with Semantic Pollution-Free Fine-tuning

摘要： 微调预训练的文本到图像（T2I）模型在定制化的人像数据集上是文本驱动的人像属性定制的主流方法。 由于微调过程中的语义污染，现有方法在定制目标属性的同时难以保持原始模型的行为并实现增量学习。 为了解决这个问题，我们提出了SPF-Portrait，这是首个在文本驱动的人像定制中纯粹理解定制语义并消除语义污染的工作。 在我们的SPF-Portrait中，我们提出了一种双路径流程，将原始模型作为传统微调路径的参考。 通过对比学习，我们确保适应目标属性，并有意识地将其他不相关的属性与原始人像对齐。 我们引入了一种新的语义感知精细控制图，该图表示目标语义的精确响应区域，以空间引导对比路径之间的对齐过程。 这一对齐过程不仅有效保留了原始模型的性能，还避免了过度对齐。 此外，我们提出了一种新的响应增强机制，在强化目标属性性能的同时，缓解直接跨模态监督中的表示差异。 大量实验表明，SPF-Portrait实现了最先进性能。 项目网页：https://spf-portrait.github.io/SPF-Portrait/ 显示英文摘要

摘要： Fine-tuning a pre-trained Text-to-Image (T2I) model on a tailored portrait dataset is the mainstream method for text-driven customization of portrait attributes. Due to Semantic Pollution during fine-tuning, existing methods struggle to maintain the original model's behavior and achieve incremental learning while customizing target attributes. To address this issue, we propose SPF-Portrait, a pioneering work to purely understand customized semantics while eliminating semantic pollution in text-driven portrait customization. In our SPF-Portrait, we propose a dual-path pipeline that introduces the original model as a reference for the conventional fine-tuning path. Through contrastive learning, we ensure adaptation to target attributes and purposefully align other unrelated attributes with the original portrait. We introduce a novel Semantic-Aware Fine Control Map, which represents the precise response regions of the target semantics, to spatially guide the alignment process between the contrastive paths. This alignment process not only effectively preserves the performance of the original model but also avoids over-alignment. Furthermore, we propose a novel response enhancement mechanism to reinforce the performance of target attributes, while mitigating representation discrepancy inherent in direct cross-modal supervision. Extensive experiments demonstrate that SPF-Portrait achieves state-of-the-art performance. Project webpage: https://spf-portrait.github.io/SPF-Portrait/