  In this paper we present a novel multi-attribute face manipulation method
based on textual descriptions. Previous text-based image editing methods either
require test-time optimization for each individual image or are restricted to
single attribute editing. Extending these methods to multi-attribute face image
editing scenarios will introduce undesired excessive attribute change, e.g.,
text-relevant attributes are overly manipulated and text-irrelevant attributes
are also changed. In order to address these challenges and achieve natural
editing over multiple face attributes, we propose a new decoupling training
scheme where we use group sampling to get text segments from same attribute
categories, instead of whole complex sentences. Further, to preserve other
existing face attributes, we encourage the model to edit the latent code of
each attribute separately via an entropy constraint. During the inference
phase, our model is able to edit new face images without any test-time
optimization, even from complex textual prompts. We show extensive experiments
and analysis to demonstrate the efficacy of our method, which generates natural
manipulated faces with minimal text-irrelevant attribute editing. Code and
pre-trained model are available at https://github.com/hwang1996/ManiCLIP.
