  Text-to-Image synthesis is the task of generating an image according to a
specific text description. Generative Adversarial Networks have been considered
the standard method for image synthesis virtually since their introduction.
Denoising Diffusion Probabilistic Models are recently setting a new baseline,
with remarkable results in Text-to-Image synthesis, among other fields. Aside
its usefulness per se, it can also be particularly relevant as a tool for data
augmentation to aid training models for other document image processing tasks.
In this work, we present a latent diffusion-based method for styled
text-to-text-content-image generation on word-level. Our proposed method is
able to generate realistic word image samples from different writer styles, by
using class index styles and text content prompts without the need of
adversarial training, writer recognition, or text recognition. We gauge system
performance with the Fr\'echet Inception Distance, writer recognition accuracy,
and writer retrieval. We show that the proposed model produces samples that are
aesthetically pleasing, help boosting text recognition performance, and get
similar writer retrieval score as real data. Code is available at:
https://github.com/koninik/WordStylist.
