paperbanana

name paperbanana description Generate publication-quality academic diagrams from paper methodology text license MIT-0 dependencies {"env":["OPENROUTER_API_KEY (recommended)","GOOGLE_API_KEY (alternative)"],"runtime":["python3","uv"]} PaperBanana Generate publication-quality academic diagrams and pipeline figures from a paper's methodology section and figure caption. PaperBanana orchestrates a multi-agent pipeline (Retriever, Planner, Stylist, Visualizer, Critic) to produce camera-ready figures suitable for venues like NeurIPS, ICML, and ACL. Environment Setup cd <repo-root> uv pip install -r requirements.txt Set your API key via environment variable or in configs/model_config.yaml . Option 1 (Recommended): OpenRouter API key — one key for both text reasoning and image generation: export OPENROUTER_API_KEY= "sk-or-v1-..." Option 2: Google API key — direct access to Gemini API: export GOOGLE_API_KEY= "your-key-here" If both keys are configured, OpenRouter is used by default. Usage python skill/run.py \ --content "METHOD_TEXT" \ --caption "FIGURE_CAPTION" \ --task diagram \ --output output.png Parameters Parameter Required Default Description --content Yes* Method section text to visualize --content-file Yes* Path to a file containing the method text (alternative to --content ) --caption Yes Figure caption or visual intent --task No diagram Task type: diagram --output No output.png Output image file path --aspect-ratio No 21:9 Aspect ratio: 21:9 , 16:9 , or 3:2 --max-critic-rounds No 3 Maximum critic refinement iterations --num-candidates No 10 Number of parallel candidates to generate --retrieval-setting No auto Retrieval mode: auto , manual , random , or none --main-model-name No gemini-3.1-pro-preview Main model for VLM agents. Provider auto-detected from configured API key --image-gen-model-name No gemini-3.1-flash-image-preview Model for image generation. Also supports gemini-3-pro-image-preview --exp-mode No demo_full Pipeline: demo_full (with Stylist) or demo_planner_critic (without Stylist) *One of --content or --content-file is required. When --num-candidates > 1, output files are named <stem>_0.png , <stem>_1.png , etc. Output The absolute path of each saved image is printed to stdout, one per line. Examples Diagram python skill/run.py \ --content "We propose a transformer-based encoder-decoder architecture. The encoder consists of 12 self-attention layers with residual connections. The decoder uses cross-attention to attend to encoder outputs and generates the target sequence autoregressively." \ --caption "Figure 1: Overview of the proposed transformer architecture" \ --task diagram \ --output architecture.png Important Notes Runtime : A single candidate typically takes 3-10 minutes depending on model and network conditions. With the default 10 candidates running in parallel, expect ~10-30 minutes total. Plan accordingly. API calls : Each candidate involves multiple LLM calls (Retriever + Planner + Stylist + Visualizer + up to 3 Critic rounds). Candidates run in parallel for efficiency. Image generation : The Visualizer agent calls an image generation model (Gemini Image) to render diagrams. About PaperBanana is based on the PaperVizAgent framework, a reference-driven multi-agent system for automated academic illustration. It was developed as part of the research paper: PaperBanana: Automating Academic Illustration for AI Scientists Dawei Zhu, Rui Meng, Yale Song, Xiyu Wei, Sujian Li, Tomas Pfister, Jinsung Yoon arXiv:2601.23265 The framework introduces a collaborative team of five specialized agents — Retriever, Planner, Stylist, Visualizer, and Critic — to transform raw scientific content into publication-quality diagrams. Evaluation is conducted on the PaperBananaBench benchmark.