This repository is a curated, annotated bibliography (awesome list) of research papers on style transfer using diffusion models. It contains no executable code, datasets, or pipelines. The value lies in taxonomizing papers into categories such as Image-Encoder Based, Swap Features in Attention Layer, Optimization-Based, LoRA-Based, Video Synthesis, 3D Generation, and Style Datasets. Each paper is listed with title, publication venue, date, and a link to its GitHub repository if available. The README also includes a brief perspective from the maintainers on the taxonomy's motivation. While the list itself is useful for researchers and practitioners in computer vision and generative models, the repository is not a functional software project and provides no reusable code or data assets.