  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
are 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} . Besides, based on FaceChain, we
further develop several applications to build a broader playground for better
showing its value, including virtual try on and 2D talking head. We hope it can
grow to serve the burgeoning needs from the communities. Note that this is an
ongoing work that will be consistently refined and improved upon. FaceChain is
open-sourced under Apache-2.0 license at
\url{https://github.com/modelscope/facechain}.
