  We present a novel image inversion framework and a training pipeline to
achieve high-fidelity image inversion with high-quality attribute editing.
Inverting real images into StyleGAN's latent space is an extensively studied
problem, yet the trade-off between the image reconstruction fidelity and image
editing quality remains an open challenge. The low-rate latent spaces are
limited in their expressiveness power for high-fidelity reconstruction. On the
other hand, high-rate latent spaces result in degradation in editing quality.
In this work, to achieve high-fidelity inversion, we learn residual features in
higher latent codes that lower latent codes were not able to encode. This
enables preserving image details in reconstruction. To achieve high-quality
editing, we learn how to transform the residual features for adapting to
manipulations in latent codes. We train the framework to extract residual
features and transform them via a novel architecture pipeline and cycle
consistency losses. We run extensive experiments and compare our method with
state-of-the-art inversion methods. Qualitative metrics and visual comparisons
show significant improvements. Code: https://github.com/hamzapehlivan/StyleRes
