FigMirror is a reference-conditioned scientific figure style transfer system that iteratively generates matplotlib scripts matching the visual style of a reference figure. It operates as a multi-agent loop (Drawer/Reviewer) orchestrated by a runner backend (Codex, Claude, or Mock), with a local web UI for upload, iteration browsing, and refinement. The system is packaged as skills for both Claude Code and Codex, plus a standalone web server. While primarily an application, it has a strong research component: the prompts, aesthetic library, and evaluation benchmarks (PlotTwin-Bench) are the result of extensive empirical iteration. The codebase demonstrates high craftsmanship in its runner architecture, atomic file operations, SSE streaming, and deterministic review gates. However, it suffers from dual skill bundles (Claude/Codex) that must stay synchronized, retired artifacts, and some incomplete features (ClaudeRunner, SSE integration).
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.