WordCon: Word-level Typography Control in Scene Text Rendering
Journal:
arXiv
Published Date:
Jun 26, 2025
Abstract
Achieving precise word-level typography control within generated images
remains a persistent challenge. To address it, we newly construct a word-level
controlled scene text dataset and introduce the Text-Image Alignment (TIA)
framework. This framework leverages cross-modal correspondence between text and
local image regions provided by grounding models to enhance the Text-to-Image
(T2I) model training. Furthermore, we propose WordCon, a hybrid
parameter-efficient fine-tuning (PEFT) method. WordCon reparameterizes
selective key parameters, improving both efficiency and portability. This
allows seamless integration into diverse pipelines, including artistic text
rendering, text editing, and image-conditioned text rendering. To further
enhance controllability, the masked loss at the latent level is applied to
guide the model to concentrate on learning the text region in the image, and
the joint-attention loss provides feature-level supervision to promote
disentanglement between different words. Both qualitative and quantitative
results demonstrate the superiority of our method to the state of the art. The
datasets and source code will be available for academic use.
