  Diffusion model based language-guided image editing has achieved great
success recently. However, existing state-of-the-art diffusion models struggle
with rendering correct text and text style during generation. To tackle this
problem, we propose a universal self-supervised text editing diffusion model
(DiffUTE), which aims to replace or modify words in the source image with
another one while maintaining its realistic appearance. Specifically, we build
our model on a diffusion model and carefully modify the network structure to
enable the model for drawing multilingual characters with the help of glyph and
position information. Moreover, we design a self-supervised learning framework
to leverage large amounts of web data to improve the representation ability of
the model. Experimental results show that our method achieves an impressive
performance and enables controllable editing on in-the-wild images with high
fidelity. Our code will be avaliable in
\url{https://github.com/chenhaoxing/DiffUTE}.
