  This study investigates identity-preserving image synthesis, an intriguing
task in image generation that seeks to maintain a subject's identity while
adding a personalized, stylistic touch. Traditional methods, such as Textual
Inversion and DreamBooth, have made strides in custom image creation, but they
come with significant drawbacks. These include the need for extensive resources
and time for fine-tuning, as well as the requirement for multiple reference
images. To overcome these challenges, our research introduces a novel approach
to identity-preserving synthesis, with a particular focus on human images. Our
model leverages a direct feed-forward mechanism, circumventing the need for
intensive fine-tuning, thereby facilitating quick and efficient image
generation. Central to our innovation is a hybrid guidance framework, which
combines stylized images, facial images, and textual prompts to guide the image
generation process. This unique combination enables our model to produce a
variety of applications, such as artistic portraits and identity-blended
images. Our experimental results, including both qualitative and quantitative
evaluations, demonstrate the superiority of our method over existing baseline
models and previous works, particularly in its remarkable efficiency and
ability to preserve the subject's identity with high fidelity.
