academic-figure-generation

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:

``bash 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:

```yaml 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).

1. 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.

Links

• PaperBanana repo: <https://github.com/dwzhu-pku/PaperBanana>
• PaperVizAgent (Google Research version of the same project): <https://github.com/google-research/papervizagent>