MasterWeaver: Taming Editability and Face Identity for Personalized Text-to-Image Generation
Yuxiang Wei, Zhilong Ji, Jinfeng Bai, Hongzhi Zhang, Lei Zhang,, Wangmeng Zuo

TL;DR
MasterWeaver is a test-time tuning-free method for personalized text-to-image generation that maintains face identity fidelity while allowing flexible editing, addressing overfitting and entanglement issues in existing models.
Contribution
It introduces a novel encoder and cross attention mechanism, along with an editing direction loss, to improve identity fidelity and editability without additional training.
Findings
Achieves faithful face identity in generated images.
Enhances text controllability over generated images.
Demonstrates superiority over existing methods in experiments.
Abstract
Text-to-image (T2I) diffusion models have shown significant success in personalized text-to-image generation, which aims to generate novel images with human identities indicated by the reference images. Despite promising identity fidelity has been achieved by several tuning-free methods, they usually suffer from overfitting issues. The learned identity tends to entangle with irrelevant information, resulting in unsatisfied text controllability, especially on faces. In this work, we present MasterWeaver, a test-time tuning-free method designed to generate personalized images with both faithful identity fidelity and flexible editability. Specifically, MasterWeaver adopts an encoder to extract identity features and steers the image generation through additional introduced cross attention. To improve editability while maintaining identity fidelity, we propose an editing direction loss for…
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Taxonomy
TopicsScientific Computing and Data Management · Multimedia Communication and Technology
MethodsDiffusion
