Face0: Instantaneously Conditioning a Text-to-Image Model on a Face

We present Face0, a novel way to instantaneously condition a text-to-image generation model on a face without any optimization procedures such as fine-tuning or inversions. We augment a dataset of annotated images with embeddings of the included faces and train an image generation model on the augmented dataset. Once trained, our system is practically identical at inference time to the underlying base model, and is therefore able to generate face-conditioned images in just a couple of seconds. Our method achieves pleasing results, is remarkably simple, extremely fast, and equips the underlying model with new capabilities, like controlling the generated images both via text or via direct manipulation of the input face embeddings. In addition, when using a fixed random vector instead of a face embedding from a user supplied image, our method essentially solves the problem of consistent character generation across images. Finally, our method decouples the model’s textual biases from its biases on faces. While requiring further research, we hope that this may help reduce biases in future text-to-image models.