TextMaster: A Unified Framework for Realistic Text Editing via Glyph-Style Dual-Control

In image editing tasks,high-quality text editing capabilities can significantly reduce both human and material resource costs.Existing methods, however,face significant limitations in terms of stroke accuracy for complex text and controllability of generated text styles.To address these challenges,we propose TextMaster,a solution capable of accurately editing text across various scenarios and image regions,while ensuring proper layout and controllable text style.Our approach incorporates adaptive standard letter spacing as guidance during training and employs adaptive mask boosting to prevent the leakage of text position and size information.By leveraging an attention mechanism to compute the intermediate layer bounding box regression loss for each character,our method enables the learning of text layout across diverse contexts.Additionally,we enhance text rendering accuracy and fidelity by injecting high-resolution standard font information and applying perceptual loss within the text editing region.Through a novel style injection technique, we achieve controllable style transfer for the injected text.Through comprehensive experiments,we demonstrate the state-of-the-art performance of our method.