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Generative Adversarial Networks (GANs) have significantly advanced imagesynthesis through mapping randomly sampled latent codes to high-fidelitysynthesized images. However, applying well-trained GANs to real image editingremains challenging. A common solution is to find an approximate latent codethat can adequately recover the input image to edit, which is also known as GANinversion. To invert a GAN model, prior works typically focus on reconstructingthe target image at the pixel level, yet few studies are conducted on whetherthe inverted result can well support manipulation at the semantic level. Thiswork fills in this gap by proposing in-domain GAN inversion, which consists ofa domain-guided encoder and a domain-regularized optimizer, to regularize theinverted code in the native latent space of the pre-trained GAN model. In thisway, we manage to sufficiently reuse the knowledge learned by GANs for imagereconstruction, facilitating a wide range of editing applications without anyretraining. We further make comprehensive analyses on the effects of theencoder structure, the starting inversion point, as well as the inversionparameter space, and observe the trade-off between the reconstruction qualityand the editing property. Such a trade-off sheds light on how a GAN modelrepresents an image with various semantics encoded in the learned latentdistribution. Code, models, and demo are available at the project page:https://genforce.github.io/idinvert/.

In-Domain GAN Inversion for Faithful Reconstruction and Editability