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
this https URL

FaceX 是一种新颖的面部通用模型，能够同时处理多样化的面部任务，通过 Facial Omni-Representation Decomposing (FORD) 实现对各种面部组件的无缝操作，并利用 StableDiffusion 的先验来增强生成质量和加速训练，在这基础上通过 Facial Omni-Representation Steering (FORS) 和 Facial Representation Controller (FRC) 实现对生成过程的有效控制。