DreamIdentity: Improved Editability for Efficient Face-identity
  Preserved Image Generation
Zhuowei Chen Shancheng Fang Wei Liu Qian He Mengqi Huang Yongdong Zhang Zhendong Mao
While large-scale pre-trained text-to-image models can synthesize diverse and
high-quality human-centric images, an intractable problem is how to preserve
the face identity for conditioned face images. Existing methods either require
time-consuming optimization for each face-identity or learning an efficient
encoder at the cost of harming the editability of models. In this work, we
present an optimization-free method for each face identity, meanwhile keeping
the editability for text-to-image models. Specifically, we propose a novel
face-identity encoder to learn an accurate representation of human faces, which
applies multi-scale face features followed by a multi-embedding projector to
directly generate the pseudo words in the text embedding space. Besides, we
propose self-augmented editability learning to enhance the editability of
models, which is achieved by constructing paired generated face and edited face
images using celebrity names, aiming at transferring mature ability of
off-the-shelf text-to-image models in celebrity faces to unseen faces.
Extensive experiments show that our methods can generate identity-preserved
images under different scenes at a much faster speed.