Low-parameter GAN inversion framework based on hypernetwork

In response to the significant parameter overhead in current Generative Adversarial Networks (GAN) inversion methods when balancing high fidelity and editability, we propose a novel lightweight inversion framework based on an optimized generator. We aim to balance fidelity and editability within the StyleGAN latent space. To achieve this, the study begins by mapping raw data to the \({W}^{+}\) latent space, enhancing the quality of the resulting inverted images. Following this mapping step, we introduce a carefully designed lightweight hypernetwork. This hypernetwork operates to selectively modify primary detailed features, thereby leading to a notable reduction in the parameter count essential for model training. By learning parameter variations, the precision of subsequent image editing is augmented. Lastly, our approach integrates a multi-channel parallel optimization computing module into the above structure to decrease the time needed for model image processing. Extensive experiments were conducted in facial and automotive imagery domains to validate our lightweight inversion framework. Results demonstrate that our method achieves equivalent or superior inversion and editing quality, utilizing fewer parameters.