  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.
