Recent advancement in personalized image generation have unveiled the
intriguing capability of pre-trained text-to-image models on learning identity
information from a collection of portrait images. However, existing solutions
can be vulnerable in producing truthful details, and usually suffer from
several defects such as (i) The generated face exhibit its own unique
characteristics, \ie facial shape and facial feature positioning may not
resemble key characteristics of the input, and (ii) The synthesized face may
contain warped, blurred or corrupted regions. In this paper, we present
FaceChain, a personalized portrait generation framework that combines a series
of customized image-generation model and a rich set of face-related perceptual
understanding models (\eg, face detection, deep face embedding extraction, and
facial attribute recognition), to tackle aforementioned challenges and to
generate truthful personalized portraits, with only a handful of portrait
images as input. Concretely, we inject several SOTA face models into the
generation procedure, achieving a more efficient label-tagging,
data-processing, and model post-processing compared to previous solutions, such
as DreamBooth ~\cite{ruiz2023dreambooth} , InstantBooth
~\cite{shi2023instantbooth} , or other LoRA-only approaches ~\cite{hu2021lora}
. Through the development of FaceChain, we have identified several potential
directions to accelerate development of Face/Human-Centric AIGC research and
application. We have designed FaceChain as a framework comprised of pluggable
components that can be easily adjusted to accommodate different styles and
personalized needs. We hope it can grow to serve the burgeoning needs from the
communities. FaceChain is open-sourced under Apache-2.0 license at
https://github.com/modelscope/facechain.

FaceChain 是一个个性化肖像生成框架，结合一系列定制图像生成模型和丰富的与人脸相关的感知理解模型，通过只有少量肖像图像作为输入，解决了现有解决方案中产生真实细节的漏洞和包含扭曲、模糊或损坏区域的合成人脸问题。