  Text-to-Image (T2I) generation methods based on diffusion model have garnered
significant attention in the last few years. Although these image synthesis
methods produce visually appealing results, they frequently exhibit spelling
errors when rendering text within the generated images. Such errors manifest as
missing, incorrect or extraneous characters, thereby severely constraining the
performance of text image generation based on diffusion models. To address the
aforementioned issue, this paper proposes a novel approach for text image
generation, utilizing a pre-trained diffusion model (i.e., Stable Diffusion
[27]). Our approach involves the design and training of a light-weight
character-level text encoder, which replaces the original CLIP encoder and
provides more robust text embeddings as conditional guidance. Then, we
fine-tune the diffusion model using a large-scale dataset, incorporating local
attention control under the supervision of character-level segmentation maps.
Finally, by employing an inference stage refinement process, we achieve a
notably high sequence accuracy when synthesizing text in arbitrarily given
images. Both qualitative and quantitative results demonstrate the superiority
of our method to the state of the art. Furthermore, we showcase several
potential applications of the proposed UDiffText, including text-centric image
synthesis, scene text editing, etc. Code and model will be available at
https://github.com/ZYM-PKU/UDiffText .
