arXiv Computer Vision
Sep 3

GlyphAnchor: Enhancing Visual Text Rendering via Position-Anchored Glyph Priors

GlyphAnchor is a new method that improves visual text rendering in image generation and editing models by adding lightweight glyph patch conditions anchored to the target image’s positional encoding. The approach is trained with staged supervised finetuning and text-aware post‑training, and it works with both text‑to‑image and image‑editing diffusion transformers. Experiments on various backbones and the newly introduced InfoTextBench benchmark show that GlyphAnchor consistently enhances text fidelity while maintaining overall image quality, especially for long, complex, or densely arranged text and rare characters.

By Qiang Xiang, Shuang Sun, Binglei Li, Yibo Chen, Xu Tang, Yao Hu, Junping Zhang
arXiv AI
Sep 16

Efficient Text-to-Image Generation: An Adaptive Step Schedule Controller for Diffusion Models

The paper introduces an adaptive step schedule controller for text‑to‑image diffusion models, allowing the number of denoising steps to vary based on the complexity of the input prompt. By mixing step schedules of different sizes and monitoring error discrepancies at each timestep, the method switches schedules to maintain image quality while reducing inference time. Experiments on COCO and DiffusionDB demonstrate that this approach achieves faster generation without sacrificing visual fidelity.

By Kuluhan Binici, Cihan Acar, Shivam Aggarwal, Siying Liu, Tulika Mitra
arXiv Computer Vision
Aug 21

TextRefine: Improving Textual Fidelity, Spatial Placement, and Glyph Rendering for Text Editing in Product Posters

arXiv:2608. 19637v1 Announce Type: new Abstract: Text editing in product posters entails inserting new text or replacing existing text while preserving product appearance, background content, and global composition.

By Honglie Wang, Jia Sun, Zijun Li, Junlong Wu, Pengcheng Wei, Jiyuan Wang, Yongrui Heng, Boheng Zhang, Huaiqing Wang, Dewen Fan, Qianqian Gan, Fan Yang, Tingting Gao, Yan-Ming Zhang
arXiv Machine Learning
Sep 15

Abstract-LoRA: Unlocking Single-Image Style Transfer through Targeted U-Net Block Training

Abstract‑LoRA introduces a lightweight LoRA training approach that targets specific U‑Net blocks in diffusion models to improve single‑image style transfer. By refining block selection, adding more blocks, and using clustering‑based style abstraction, it better disentangles and balances style and content compared to prior methods like B‑LoRA. Experiments show that the method produces more harmonious artistic images while quantitatively preserving both style and content.

By Xinglin Hu