Character-Adapter: Prompt-Guided Region Control for High-Fidelity Character Customization
This addresses high-fidelity character customization for applications like storytelling and portrait generation, representing a strong specific gain in a domain-specific area.
The paper tackles the problem of generating images with consistent characters, which is challenging due to inadequate feature extraction and concept confusion, and proposes Character-Adapter, a plug-and-play framework that achieves state-of-the-art performance with a 24.8% improvement over other methods.
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-Adapter.