Abstract
Customized image generation, which seeks to synthesize images with consistent characters, holds significant relevance for applications such as storytelling, portrait generation, and character design. However, previous approaches have encountered challenges in preserving characters with high-fidelity consistency due to inadequate feature extraction and concept confusion of reference characters. Therefore, we propose Character-Adapter, a plug-and-play framework designed to generate images that preserve the details of reference characters, ensuring high-fidelity consistency. Character-Adapter employs prompt-guided segmentation to ensure fine-grained regional features of reference characters and dynamic region-level adapters to mitigate concept confusion. Extensive experiments are conducted to validate the effectiveness of Character-Adapter. Both quantitative and qualitative results demonstrate that Character-Adapter achieves the state-of-the-art performance of consistent character generation, with an improvement of 24.8% compared with other methods
Abstract (translated)
定制图像生成，旨在合成具有一致性字符的图像，对于诸如叙事、肖像生成和角色设计等应用具有重要的相关性。然而，由于参考角色的特征提取不足和参考角色概念混淆，以前的方法在保留高保真度特征方面遇到了挑战。因此，我们提出了Character-Adapter，一个可插拔和使用的框架，旨在生成保留参考角色详细信息的图像，确保高保真度一致性。Character-Adapter采用提示引导分割来确保参考角色的细粒度区域特征，并使用动态区域级别适应器来缓解概念混淆。 extensive实验验证了Character-Adapter的有效性。量化结果表明，Character-Adapter实现了与其他方法相当的最佳性能，性能提高了24.8%。
URL
https://arxiv.org/abs/2406.16537
