  We propose LatentSwap, a simple face swapping framework generating a face
swap latent code of a given generator. Utilizing randomly sampled latent codes,
our framework is light and does not require datasets besides employing the
pre-trained models, with the training procedure also being fast and
straightforward. The loss objective consists of only three terms, and can
effectively control the face swap results between source and target images. By
attaching a pre-trained GAN inversion model independent to the model and using
the StyleGAN2 generator, our model produces photorealistic and high-resolution
images comparable to other competitive face swap models. We show that our
framework is applicable to other generators such as StyleNeRF, paving a way to
3D-aware face swapping and is also compatible with other downstream StyleGAN2
generator tasks. The source code and models can be found at
\url{https://github.com/usingcolor/LatentSwap}.
