InnoText: A Unified Model for Visual Text Generation and Editing

Diffusion models have recently achieved remarkable successin high-fidelity image synthesis, yet their application to visual text gener-ation and editing remains relatively underexplored. Unlike general imagegeneration, visual text tasks demand precise structural regularity andlegibility, which may pose additional challenges for small-scale text andnon-Latin scripts such as Chinese. Existing UNet-based models oftenstruggle to produce clear and coherent text, while DiT-based models,though more expressive, are typically limited to a single task, whichmay lead to redundant training pipelines, inconsistent visual styles, andreduced cross-task generalization. To address these challenges, we pro-pose InnoText, a unified DiT-based framework capable of performingboth text generation and editing within a single model. We introduce aFont Size-Aware Modulation (FSAM) module to enhance representationsacross font scales, a Small-Character Aware Augmentation strategy toimprove fine-grained fidelity, and a Task-Specific Region Weighted Lossfor adaptive optimization. To support training and evaluation, we alsoconstruct a high-quality bilingual (English-Chinese) visual text datasetcovering diverse fonts, sizes, and backgrounds. Experimental results demon-strate that our method achieves superior generation accuracy and editingquality, producing visually appealing and realistic text images.