FLUX-Text: A Simple and Advanced Diffusion Transformer Baseline for Scene Text Editing
Journal:
arXiv
Published Date:
May 6, 2025
Abstract
The task of scene text editing is to modify or add texts on images while
maintaining the fidelity of newly generated text and visual coherence with the
background. Recent works based on latent diffusion models (LDM) show improved
text editing results, yet still face challenges and often generate inaccurate
or unrecognizable characters, especially for non-Latin ones (\eg, Chinese),
which have complex glyph structures. To address these issues, we present
FLUX-Text, a simple and advanced multilingual scene text editing framework
based on FLUX-Fill. Specifically, we carefully investigate glyph conditioning,
considering both visual and textual modalities. To retain the original
generative capabilities of FLUX-Fill while enhancing its understanding and
generation of glyphs, we propose lightweight glyph and text embedding modules.
Owning to the lightweight design, FLUX-Text is trained only with $100K$
training examples compared to current popular methods trained with 2.9M ones.
With no bells and whistles, our method achieves state-of-the-art performance on
text editing tasks. Qualitative and quantitative experiments on the public
datasets demonstrate that our method surpasses previous works in text fidelity.
