  Visual text generation has significantly advanced through diffusion models
aimed at producing images with readable and realistic text. Recent works
primarily use a ControlNet-based framework, employing standard font text images
to control diffusion models. Recognizing the critical role of control
information in generating high-quality text, we investigate its influence from
three perspectives: input encoding, role at different stages, and output
features. Our findings reveal that: 1) Input control information has unique
characteristics compared to conventional inputs like Canny edges and depth
maps. 2) Control information plays distinct roles at different stages of the
denoising process. 3) Output control features significantly differ from the
base and skip features of the U-Net decoder in the frequency domain. Based on
these insights, we propose TextGen, a novel framework designed to enhance
generation quality by optimizing control information. We improve input and
output features using Fourier analysis to emphasize relevant information and
reduce noise. Additionally, we employ a two-stage generation framework to align
the different roles of control information at different stages. Furthermore, we
introduce an effective and lightweight dataset for training. Our method
achieves state-of-the-art performance in both Chinese and English text
generation. The code and dataset available at
https://github.com/CyrilSterling/TextGen.
