Hugging Face Trending Papers
Aug 12

Through Van Gogh's Eyes: Global Style Transfer with Diffusion Mod

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 Computer Vision
Oct 22

Loomis Painter: Learning Painting Processes by Reversing Image-to-Video Generation

Loomis Painter is a framework that learns painting processes by training on reversed image‑to‑video sequences, preserving reference‑attention behavior and avoiding long‑range dependencies that disrupt pretrained models. It introduces a media‑transfer strategy to decouple input appearance from the target process, enabling generation across different artistic media. Evaluated on a curated dataset of real‑world painting processes, the method outperforms baselines on LPIPS, DINO, and CLIP metrics and receives consistent preference in expert and non‑expert user studies.

By Markus Pobitzer, Chang Liu, Chenyi Zhuang, Teng Long, Bin Ren, Nicu Sebe
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