IDAdapter: Learning mixed features for tuning-free personalization of text-to-image models
Author(s)
Type
Conference Paper
Abstract
Leveraging Stable Diffusion for the generation of personalized portraits has emerged as a powerful and noteworthy tool, enabling users to create high-fidelity, custom character avatars based on their specific prompts. However, existing personalization methods face challenges, including test-time fine-tuning, the requirement of multiple input images, low preservation of identity, and limited diversity in generated outcomes. To overcome these challenges, we introduce IDAdapter, a tuning-free approach that enhances the diversity and identity preservation in personalized image generation from a single face image. IDAdapter integrates a personalized concept into the generation process through a combination of textual and visual injections and a face identity loss. During the training phase, we incorporate mixed features from multiple reference images of a specific identity to enrich identity-related content details, guiding the model to generate images with more diverse styles, expressions, and angles. Extensive evaluations demonstrate the effectiveness of our method, achieving both diversity and identity fidelity.
Date Issued
2024-09-27
Date Acceptance
2024-06-17
Citation
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2024, pp.950-959
ISSN
2160-7508
Publisher
IEEE
Start Page
950
End Page
959
Journal / Book Title
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Copyright Statement
© 2024 IEEE. This CVPR Workshop paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.
Source
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Publication Status
Published
Start Date
2024-06-17
Finish Date
2024-06-18
Coverage Spatial
Seattle, WA, USA