Current face reenactment and swapping methods mainly rely on GAN frameworks,
but recent focus has shifted to pre-trained diffusion models for their superior
generation capabilities. However, training these models is resource-intensive,
and the results have not yet achieved satisfactory performance levels. To
address this issue, we introduce Face-Adapter, an efficient and effective
adapter designed for high-precision and high-fidelity face editing for
pre-trained diffusion models. We observe that both face reenactment/swapping
tasks essentially involve combinations of target structure, ID and attribute.
We aim to sufficiently decouple the control of these factors to achieve both
tasks in one model. Specifically, our method contains: 1) A Spatial Condition
Generator that provides precise landmarks and background; 2) A Plug-and-play
Identity Encoder that transfers face embeddings to the text space by a
transformer decoder. 3) An Attribute Controller that integrates spatial
conditions and detailed attributes. Face-Adapter achieves comparable or even
superior performance in terms of motion control precision, ID retention
capability, and generation quality compared to fully fine-tuned face
reenactment/swapping models. Additionally, Face-Adapter seamlessly integrates
with various StableDiffusion models.

通过引入高效有效的 Face-Adapter，我们旨在解决资源密集型的训练问题，从而实现预训练扩散模型的高精度和高保真度的人脸编辑，该模型在运动控制精度、ID 保留能力和生成质量方面具有可比甚至优越的表现。