← Back to all news
arXiv Computer Vision By Mingxuan Cui, Jingpu Yang, Fengxian Ji, Qian Jiang, Zhecheng Shi, Jiaming Wang, Zirui Song, Zhuohan Xie, Fajri Koto, Xiuying Chen

TextAlign: Preference Alignment for Text Rendering with Hierarchical Rewards

Read the original on arXiv Computer Vision →

TextAlign is a post‑training preference‑alignment framework that improves text rendering in large text‑to‑image generative models without changing the generator architecture. It uses a hierarchical vision‑language model to reward global, word, and glyph‑level accuracy, converting binary defect judgments into a scalar preference signal that can be optimized with GRPO or DPO. Experiments on FLUX.1‑dev and Z‑Image‑Turbo demonstrate higher OCR‑based text accuracy while preserving overall generation quality, outperforming several foundation and text‑rendering baselines.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

Related stories

arXiv Computer Vision
Oct 16

Blame Errant Words, Not the Canvas: Defect-Aware Spatial Credit Assignment for Visual Text Generation

The paper introduces TextDART, a defect-aware reward model that evaluates individual text regions for semantic correctness and glyph quality, and SCANNER, a method that uses these regional judgments to provide spatially differentiated policy feedback. This approach addresses limitations of previous reinforcement learning methods that overlook glyph defects and obscure regional quality. Experiments on existing benchmarks and the new PolyText-Bench show state‑of‑the‑art performance in text accuracy and glyph fidelity.

By Sujie Hu, Huaisong Zhang, Pujing Yang, Qian Wang, Xiaomin Li, Xinhang Leng, Chen Li, Jing Lyu, Xiu Li, Ling Pan
reinforcement-learningbenchmarks
More like this →
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
llmsdiffusionfine-tuningbenchmarks
More like this →