  To achieve disentangled image manipulation, previous works depend heavily on
manual annotation. Meanwhile, the available manipulations are limited to a
pre-defined set the models were trained for. We propose a novel framework,
i.e., Predict, Prevent, and Evaluate (PPE), for disentangled text-driven image
manipulation that requires little manual annotation while being applicable to a
wide variety of manipulations. Our method approaches the targets by deeply
exploiting the power of the large-scale pre-trained vision-language model CLIP.
Concretely, we firstly Predict the possibly entangled attributes for a given
text command. Then, based on the predicted attributes, we introduce an
entanglement loss to Prevent entanglements during training. Finally, we propose
a new evaluation metric to Evaluate the disentangled image manipulation. We
verify the effectiveness of our method on the challenging face editing task.
Extensive experiments show that the proposed PPE framework achieves much better
quantitative and qualitative results than the up-to-date StyleCLIP baseline.
