  Recent advances in text-to-image models have opened new frontiers in
human-centric generation. However, these models cannot be directly employed to
generate images with consistent newly coined identities. In this work, we
propose CharacterFactory, a framework that allows sampling new characters with
consistent identities in the latent space of GANs for diffusion models. More
specifically, we consider the word embeddings of celeb names as ground truths
for the identity-consistent generation task and train a GAN model to learn the
mapping from a latent space to the celeb embedding space. In addition, we
design a context-consistent loss to ensure that the generated identity
embeddings can produce identity-consistent images in various contexts.
Remarkably, the whole model only takes 10 minutes for training, and can sample
infinite characters end-to-end during inference. Extensive experiments
demonstrate excellent performance of the proposed CharacterFactory on character
creation in terms of identity consistency and editability. Furthermore, the
generated characters can be seamlessly combined with the off-the-shelf
image/video/3D diffusion models. We believe that the proposed CharacterFactory
is an important step for identity-consistent character generation. Project page
is available at: https://qinghew.github.io/CharacterFactory/.
