  We present a novel high-fidelity generative adversarial network (GAN)
inversion framework that enables attribute editing with image-specific details
well-preserved (e.g., background, appearance, and illumination). We first
analyze the challenges of high-fidelity GAN inversion from the perspective of
lossy data compression. With a low bit-rate latent code, previous works have
difficulties in preserving high-fidelity details in reconstructed and edited
images. Increasing the size of a latent code can improve the accuracy of GAN
inversion but at the cost of inferior editability. To improve image fidelity
without compromising editability, we propose a distortion consultation approach
that employs a distortion map as a reference for high-fidelity reconstruction.
In the distortion consultation inversion (DCI), the distortion map is first
projected to a high-rate latent map, which then complements the basic low-rate
latent code with more details via consultation fusion. To achieve high-fidelity
editing, we propose an adaptive distortion alignment (ADA) module with a
self-supervised training scheme, which bridges the gap between the edited and
inversion images. Extensive experiments in the face and car domains show a
clear improvement in both inversion and editing quality.
