  This work presents FaceX framework, a novel facial generalist model capable
of handling diverse facial tasks simultaneously. To achieve this goal, we
initially formulate a unified facial representation for a broad spectrum of
facial editing tasks, which macroscopically decomposes a face into fundamental
identity, intra-personal variation, and environmental factors. Based on this,
we introduce Facial Omni-Representation Decomposing (FORD) for seamless
manipulation of various facial components, microscopically decomposing the core
aspects of most facial editing tasks. Furthermore, by leveraging the prior of a
pretrained StableDiffusion (SD) to enhance generation quality and accelerate
training, we design Facial Omni-Representation Steering (FORS) to first
assemble unified facial representations and then effectively steer the SD-aware
generation process by the efficient Facial Representation Controller (FRC).
%Without any additional features, Our versatile FaceX achieves competitive
performance compared to elaborate task-specific models on popular facial
editing tasks. Full codes and models will be available at
https://github.com/diffusion-facex/FaceX.
