  The rapid advancement in image generation models has predominantly been
driven by diffusion models, which have demonstrated unparalleled success in
generating high-fidelity, diverse images from textual prompts. Despite their
success, diffusion models encounter substantial challenges in the domain of
image editing, particularly in executing disentangled edits-changes that target
specific attributes of an image while leaving irrelevant parts untouched. In
contrast, Generative Adversarial Networks (GANs) have been recognized for their
success in disentangled edits through their interpretable latent spaces. We
introduce GANTASTIC, a novel framework that takes existing directions from
pre-trained GAN models-representative of specific, controllable attributes-and
transfers these directions into diffusion-based models. This novel approach not
only maintains the generative quality and diversity that diffusion models are
known for but also significantly enhances their capability to perform precise,
targeted image edits, thereby leveraging the best of both worlds.
