PortraitBooth: A Versatile Portrait Model for Fast Identity-preserved Personalization
This addresses the problem of slow and identity-distorted personalized image generation for users of diffusion models, offering a more practical solution.
The paper tackles the inefficiency and identity distortion in personalized image generation by proposing PortraitBooth, a method that achieves fast, identity-preserved, and expression-editable text-to-image generation without fine-tuning, demonstrating superior performance over state-of-the-art methods.
Recent advancements in personalized image generation using diffusion models have been noteworthy. However, existing methods suffer from inefficiencies due to the requirement for subject-specific fine-tuning. This computationally intensive process hinders efficient deployment, limiting practical usability. Moreover, these methods often grapple with identity distortion and limited expression diversity. In light of these challenges, we propose PortraitBooth, an innovative approach designed for high efficiency, robust identity preservation, and expression-editable text-to-image generation, without the need for fine-tuning. PortraitBooth leverages subject embeddings from a face recognition model for personalized image generation without fine-tuning. It eliminates computational overhead and mitigates identity distortion. The introduced dynamic identity preservation strategy further ensures close resemblance to the original image identity. Moreover, PortraitBooth incorporates emotion-aware cross-attention control for diverse facial expressions in generated images, supporting text-driven expression editing. Its scalability enables efficient and high-quality image creation, including multi-subject generation. Extensive results demonstrate superior performance over other state-of-the-art methods in both single and multiple image generation scenarios.