Title: LAMDA: Latent mapping for domain adaption of image generators. Abstract: Our paper tackles the problem of adapting image generators to new keyword-defined domains without training on any new images. We combine the power of CLIP models for image-text similarity with the disentangled representation of images found in the latent spaces of generative adversarial networks (GANs). We present the latent mapper (LAMDA) which maps directions in CLIP text space to directions in the GAN latent space. Using a latent mapper enables training on a large number of keywords simultaneously which was not previously possible, and allows benefiting from the inter-relation of different keywords. It also leads to higher image quality while requiring only a fraction of the training time and parameters of state-of-the-art methods. Our method allows the network to learn the relationship between words and latent spaces. As a result it allows the composition of words to generate semantically meaningful images. Additionally, it allows adaptation to some unseen words after training. We perform extensive analysis to validate the advantages of our method quantitatively and qualitatively.