Image style is a highly abstract, human-constructed concept shaped by a range of visual factors and intrinsically entangled with content, yet a unified and explicit definition of image style remains l...
The paper introduces Abstraction in Style (AiS), a generative framework that separates structural abstraction from visual stylization. AiS first creates an abstraction proxy that reinterprets a target image’s structure according to the abstraction logic of a given style, then renders this proxy to produce a stylized output. By learning transformations from visual exemplars without explicit geometric supervision, AiS enables a broader range of stylistic transformations, improves controllability, and allows more expressive stylization.
By Min Lu, Yuanfeng He, Anthony Chen, Jianhuang He, Pu Wang, Daniel Cohen-Or, Hui Huang
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.
arXiv:2412.12048v2 Announce Type: replace
Abstract: Low-Rank Adaptation (LoRA) has become a standard approach for parameter-efficient fine-tuning of image diffusion models, with over 200K models trai...
By Chenxi Liu, Towaki Takikawa, Alec Jacobson
arXiv:2607. 04147v1 Announce Type: cross Abstract: Automated fine-grained perception of calligraphy styles--a task vital to cultural heritage preservation--remains a critical challenge for Large Vision-Language Models (LVLMs), largely constrained by existing datasets that suffer from modal mixture and flattened labels.
By Yinsheng Yao, Yan Liu, Chen Ye