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

翻译：定制化图像生成旨在合成具有一致角色的图像，对于故事叙述、肖像生成和角色设计等应用具有重要意义。然而，由于参考角色特征提取不足及概念混淆，先前方法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，旨在生成保留参考角色细节的图像，确保高保真一致性。Character-Adapter采用提示引导分割技术以保证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。我们进行了大量实验以验证Character-Adapter的有效性。定量与定性结果均表明，Character-Adapter在一致角色生成任务上达到了最先进的性能，相较于其他方法提升了24.8%。