  We present a new multi-modal face image generation method that converts a
text prompt and a visual input, such as a semantic mask or scribble map, into a
photo-realistic face image. To do this, we combine the strengths of Generative
Adversarial networks (GANs) and diffusion models (DMs) by employing the
multi-modal features in the DM into the latent space of the pre-trained GANs.
We present a simple mapping and a style modulation network to link two models
and convert meaningful representations in feature maps and attention maps into
latent codes. With GAN inversion, the estimated latent codes can be used to
generate 2D or 3D-aware facial images. We further present a multi-step training
strategy that reflects textual and structural representations into the
generated image. Our proposed network produces realistic 2D, multi-view, and
stylized face images, which align well with inputs. We validate our method by
using pre-trained 2D and 3D GANs, and our results outperform existing methods.
Our project page is available at
https://github.com/1211sh/Diffusion-driven_GAN-Inversion/.
