ConsistentID: Portrait Generation with Multimodal Fine-Grained Identity
  Preserving
Authors: name: Jiehui Huang; name: Xiao Dong; name: Wenhui Song; name: Hanhui Li; name: Jun Zhou; name: Yuhao Cheng; name: Shutao Liao; name: Long Chen; name: Yiqiang Yan; name: Shengcai Liao; name: Xiaodan Liang
Published: 2024-04-25 17:23:43

Abstract: Diffusion-based technologies have made significant strides, particularly in
personalized and customized facialgeneration. However, existing methods face
challenges in achieving high-fidelity and detailed identity (ID)consistency,
primarily due to insufficient fine-grained control over facial areas and the
lack of a comprehensive strategy for ID preservation by fully considering
intricate facial details and the overall face. To address these limitations, we
introduce ConsistentID, an innovative method crafted for
diverseidentity-preserving portrait generation under fine-grained multimodal
facial prompts, utilizing only a single reference image. ConsistentID comprises
two key components: a multimodal facial prompt generator that combines facial
features, corresponding facial descriptions and the overall facial context to
enhance precision in facial details, and an ID-preservation network optimized
through the facial attention localization strategy, aimed at preserving ID
consistency in facial regions. Together, these components significantly enhance
the accuracy of ID preservation by introducing fine-grained multimodal ID
information from facial regions. To facilitate training of ConsistentID, we
present a fine-grained portrait dataset, FGID, with over 500,000 facial images,
offering greater diversity and comprehensiveness than existing public facial
datasets. % such as LAION-Face, CelebA, FFHQ, and SFHQ. Experimental results
substantiate that our ConsistentID achieves exceptional precision and diversity
in personalized facial generation, surpassing existing methods in the MyStyle
dataset. Furthermore, while ConsistentID introduces more multimodal ID
information, it maintains a fast inference speed during generation.