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. Our code will be released at https://github.com/Character-Adapter/Character-Adapte

翻译：定制化图像生成旨在合成具有一致角色的图像，在故事叙述、肖像生成和角色设计等应用中具有重要意义。然而，现有方法因参考角色特征提取不足及概念混淆问题，在保持角色高保真一致性方面面临挑战。为此，我们提出Character-Adapter——一种即插即用框架，旨在生成能保留参考角色细节、确保高保真一致性的图像。该框架采用提示引导分割技术保障参考角色的细粒度区域特征，并利用动态区域级适配器缓解概念混淆问题。我们通过大量实验验证了Character-Adapter的有效性，定量与定性结果均表明该方法在角色一致性生成方面达到最先进水平，相较其他方法性能提升24.8%。代码已发布于https://github.com/Character-Adapter/Character-Adapte。