UniPortrait: A Unified Framework for Identity-Preserving Single- and
  Multi-Human Image Personalization
Authors: name: Junjie He, name: Yifeng Geng, name: Liefeng Bo

This paper presents UniPortrait, an innovative human image personalization
framework that unifies single- and multi-ID customization with high face
fidelity, extensive facial editability, free-form input description, and
diverse layout generation. UniPortrait consists of only two plug-and-play
modules: an ID embedding module and an ID routing module. The ID embedding
module extracts versatile editable facial features with a decoupling strategy
for each ID and embeds them into the context space of diffusion models. The ID
routing module then combines and distributes these embeddings adaptively to
their respective regions within the synthesized image, achieving the
customization of single and multiple IDs. With a carefully designed two-stage
training scheme, UniPortrait achieves superior performance in both single- and
multi-ID customization. Quantitative and qualitative experiments demonstrate
the advantages of our method over existing approaches as well as its good
scalability, e.g., the universal compatibility with existing generative control
tools. The project page is at
https://aigcdesigngroup.github.io/UniPortrait-Page/ .