Abstract
While diffusion models have demonstrated remarkable generative capabilities, existing style transfer techniques often struggle to maintain identity while achieving high-quality stylization. This limitation is particularly acute for images where faces are small or exhibit significant camera-to-face distances, frequently leading to inadequate identity preservation. To address this, we introduce a novel, training-free framework for identity-preserved stylized image synthesis using diffusion models. Key contributions include: (1) the "Mosaic Restored Content Image" technique, significantly enhancing identity retention, especially in complex scenes; and (2) a training-free content consistency loss that enhances the preservation of fine-grained content details by directing more attention to the original image during stylization. Our experiments reveal that the proposed approach substantially surpasses the baseline model in concurrently maintaining high stylistic fidelity and robust identity integrity, particularly under conditions of small facial regions or significant camera-to-face distances, all without necessitating model retraining or fine-tuning.
Abstract (translated)
虽然扩散模型展示了出色的生成能力，但现有的风格转换技术往往难以在保持身份的同时实现高质量的风格化。这一限制尤其明显于面部较小或相机与脸部距离较大的图像中，常常导致身份保存不充分。为了解决这个问题，我们提出了一种新颖且无需训练的身份保留式风格化图像合成框架，利用扩散模型。主要贡献包括： 1. **“马赛克恢复内容图”技术**：该技术显著增强了在复杂场景中的身份保持能力。 2. **无训练的内容一致性损失**：这一损失函数通过更注重原图来增强细粒度内容细节的保留，在风格化过程中引导更多的注意力。 我们的实验结果显示，所提出的方法能够在同时维持高风格保真度和稳健的身份完整性方面显著超越基线模型，尤其是在面部区域较小或相机与脸部距离较大的情况下。值得注意的是，这些改进无需对模型进行重新训练或微调即可实现。
URL
https://arxiv.org/abs/2506.06802
