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academic-figure-generation

Design & creation

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Updated 10-15 23:43

About this skill

Academic Figure Generation

Thin CLI wrapper around PaperBanana (a.k.a. PaperVizAgent), a multi-agent figure-generation pipeline for academic papers.

The skill provides exactly one script: scripts/generate.py. It feeds your method text + caption into PaperBanana and writes N candidate PNGs. Model selection and API keys come from PaperBanana's own configs/model_config.yaml — the wrapper does not override them.

One-time setup

  1. Clone PaperBanana somewhere convenient:

    git clone https://github.com/dwzhu-pku/PaperBanana.git ~/PaperBanana
cd ~/PaperBanana
uv venv && uv pip install -r requirements.txt

  1. Configure configs/model_config.yaml — set the image model and the matching API key. Two common setups:

    defaults:
  image_model_name: "gemini-3-pro-image-preview"   # or "openai/gpt-5.4-image-2"
  model_name: "gemini-3.1-pro-preview"             # text model for Planner/Stylist/Critic

api_keys:
  google_api_key: "..."        # required for Gemini models
  openrouter_api_key: ""       # required for openai/gpt-5.4-image-2

    Use Gemini if you have a Google AI key; use GPT-Image-2 via OpenRouter if you have an OpenRouter key. Pick one — there's nothing else to wire up.

Workflow

Step 1: Gather inputs

You need:

  1. Method text: the relevant section of the paper describing the approach (./method.md or ./method.tex).
  2. Figure caption: the target caption, e.g. "Figure 1: Overview of our framework".

If the user only gives a vague request, ask:

  • What aspect of the method should the figure focus on?
  • Style? (block diagram, flowchart, pipeline, architecture, comparison)
  • Venue / column width? (ACL ≤ 7.5", NeurIPS single-column 5.5")

Step 2: Generate

~/PaperBanana/.venv/bin/python scripts/generate.py \
  --paperbanana-root ~/PaperBanana \
  --method-file ./method.md \
  --caption "Figure 1: Overview of our framework" \
  --out-dir ./figures/v1 \
  --candidates 3 \
  --aspect-ratio 16:9
Flag Default Notes
--paperbanana-root (required) Path to your PaperBanana checkout
--method-file (required) Method section as a text/markdown file
--caption (required) Target figure caption
--out-dir (required) Where PNGs land
--candidates 3 Independent diagram candidates
--max-concurrent 2 Cap concurrent runs (be gentle on quota)
--exp-mode demo_full Full pipeline (Planner+Stylist+Visualizer+Critic). Use demo_planner_critic to skip Stylist, or vanilla for single-shot.
--aspect-ratio 16:9 One of 21:9, 16:9, 3:2, 1:1
--max-critic-rounds 2 Critique → revise loops (early-exits if critic says "No changes needed")

Step 3: Present & iterate

  • Show all candidates to the user.
  • Common refinements: color scheme, layout, label text, font size.
  • Re-run with a tweaked caption or more candidates.

Step 4: Export

  • PNGs are written as candidate_0.png, candidate_1.png, … in --out-dir.
  • For camera-ready PDFs: magick candidate_0.png candidate_0.pdf.

Style guidelines

  • Color: consistent, colorblind-friendly palette
  • Fonts: match the paper's body font (Times for ACL/EMNLP, Helvetica/Arial for many ML venues)
  • Labels: concise; no full sentences inside the diagram
  • Arrows: solid for data flow, dashed for optional / feedback loops
  • Whitespace: don't overcrowd — reviewers skim figures in seconds

Common figure types

Type When to use Key elements
Pipeline / Flowchart Sequential processing Boxes + arrows, L→R or T→B
Architecture System overview Nested boxes, clear module boundaries
Comparison Before/after, baseline vs proposed Side-by-side panels
Ablation Component contributions Bar charts, highlighted rows
Framework High-level conceptual overview Abstract shapes, minimal detail

Troubleshooting

  • 429 RESOURCE_EXHAUSTED on Gemini: monthly Google AI Studio spending cap hit. Raise it at https://ai.studio/spend or switch image_model_name to openai/gpt-5.4-image-2 and set OPENROUTER_API_KEY.
  • OpenRouter Client not initialized: OPENROUTER_API_KEY not in env and openrouter_api_key not in yaml.
  • No PNGs in output dir: check out_dir/results.json for the raw per-candidate response and any error messages.
  • Long latency (>5 min): most wall time is the image model. Lower --candidates or use --exp-mode vanilla for faster iteration.

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