CoDeFont is a new one-shot font generation framework that learns to synthesize target glyphs by transferring style from a single reference glyph while preserving the target’s structure. It achieves this through multi-level style‑content factorization and progressive style‑content reconciliation, creating factor‑specific representations and using a contrastive objective to separate style and content embeddings. The method is evaluated on Bengali and Latin scripts, showing consistent improvements over existing state‑of‑the‑art approaches for both seen and unseen font styles.
By Rejoy Chakraborty, Prasun Roy, Saumik Bhattacharya, Umapada Pal
Few-shot font generation simultaneously requires global structural completeness and fine-grained local style fidelity. Existing methods usually either rely on global content-style modeling, which is robust but imperfectly disentangled, or emphasize component/local modeling, which captures fine details but relies heavily on local priors and reference coverage.
arXiv:2606. 13382v1 Announce Type: cross Abstract: Few-shot font generation simultaneously requires global structural completeness and fine-grained local style fidelity.
By Zian Yang, Zixin Wang
arXiv:2609.38636v1 Announce Type: new
Abstract: We introduce Scene Text Editing with Preserved Style (STEPS), a novel diffusion model architecture for quality text replacement in images. Scene Text E...
By Nicolas Thiebaut, Nameer Hirschkind, Xiao Yu, Kyle Spence
LoGAN is a VLM-based agentic framework designed for few-shot multilingual font localization. It takes a handful of glyphs or logo letters and generates complete character sets across many languages, including CJK, by combining a glyph-level diffusion model, style finetuning, spacing/kerning transfer, and texture expansion. The method outperforms specialized font generators and state‑of‑the‑art image editors in glyph fidelity, style, texture, and kerning consistency on datasets covering more than 27 languages.
By Zhuoning Yuan, Ta-Ying Cheng, Benjamin Klein
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
Artistic image synthesis aims to recreate the expressive visual identity of a target artist, yet existing methods often fail to capture an artist's global style. Conventional style transfer methods transfer the style of one or a few reference artworks to a content image in a One-to-One manner, making them effective for artwork-level stylization but limited in representing the broader stylistic distribution of an artist.
SafeStyle is a training‑free framework that injects calibrated style residuals into frozen diffusion models for reference‑guided stylization. It estimates style‑supported and content‑associated subspaces from small calibration sets, then transports purified style evidence across adaptive spatial granularity while limiting its influence with a residual‑norm budget. Experiments on texture‑ and geometry‑dominant styles show high DINO style similarity (0.432–0.474) with minimal semantic leakage (0.8%).
By Zhangping Yang, Min Li, Song Yan, Rong Gao, Xinliang Bi, Guanye Xiong, Yujie He
arXiv:2606. 05261v1 Announce Type: cross Abstract: Variable fonts enable continuous variation of glyph geometry along semantic design axes such as weight, width, slant, and optical size.
By Nadav Benedek, Ariel Shamir, Ohad Fried
arXiv:2610.07684v1 Announce Type: cross
Abstract: Personalized image generation aims to synthesize text-driven images conditioned on reference images, while mainly casting the generation as image cus...
By Haipeng Liu, Yang Wang, Meng Wang
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
MOSS-Image is an open-weight image generation system that combines a 5B single-stream diffusion transformer with an intention-aware prompt enhancement module to produce high-fidelity, text-rich images with precise layout control. The system supports bilingual long-form text rendering and introduces evaluation benchmarks LongLongTextBench and RealLayoutArena. It achieves state‑of‑the‑art performance on LLTB and ranks highly on RLA, with plans to release the model, training recipe, and evaluation framework for the community.
By MOSI. AI