| name | nano-banana-image-gen |
| description | Use when the user asks to generate or edit images with Nano Banana / Nano Banana Pro (users often call these models 'banana'). Nano Banana (aka Nano Banana 2) maps to `gemini-3.1-flash-image-preview`; Nano Banana Pro maps to `gemini-3-pro-image`. |
Nano Banana Image Generation Skill
Generate or edit images using Google's Gemini native image generation. Naming note: Nano Banana (aka Nano Banana 2) maps to gemini-3.1-flash-image-preview, and Nano Banana Pro maps to gemini-3-pro-image (currently exposed in this CLI as gemini-3-pro-image-preview).
When to use
- Generate a new image (concept art, product shot, cover, hero, sticker, icon)
- Edit an existing image (style change, object removal, background replacement, text localization)
- Multi-image composition (combine up to 14 reference images)
- Search-grounded generation (real-time data like weather, events, charts)
Model selection
gemini-3.1-flash-image-preview (Nano Banana / Nano Banana 2, default): best speed/quality balance, supports Thinking + Search Grounding, and supports 512px low-latency mode. Best with <=3 input images.gemini-3-pro-image-preview (Nano Banana Pro / gemini-3-pro-image): professional asset production, advanced text rendering, Thinking mode, up to 4K, Google Search grounding. Supports up to 14 input images (5 humans high-fidelity, 6 objects high-fidelity).
Use Pro when the user needs: highest text rendering quality, complex multi-image composition, or explicitly requests "pro" / "high quality".
Decision tree
- If the user provides input image(s) or says "edit/retouch/change/modify/localize" -> edit
- If the user needs Search-grounded imagery (current weather, live data) -> generate with --google-search
- Else -> generate
Workflow
- Decide intent: generate vs edit (see decision tree).
- Collect inputs: prompt, input images (if any), constraints, text to render (verbatim).
- Choose model: Flash (default) or Pro (see model selection above).
- Augment prompt into a structured spec (see prompt augmentation below); do not invent new creative requirements.
- Run the bundled CLI (
scripts/gemini_image_gen.py). See --help for commands and flags.
- Inspect outputs and validate: subject, style, composition, text accuracy.
- Iterate: make a single targeted change, re-run, re-check.
- Save final outputs and note the final prompt + flags used.
Output conventions
- Write final artifacts under
output/imagegen/ when working in this repo.
- Use
--out to control output path; keep filenames stable and descriptive.
Dependencies
cd {SKILL_BASE_DIR} && uv run --with google-genai --with google-auth --with pillow scripts/gemini_image_gen.py generate --prompt "test" --dry-run
Defaults & rules
- Model:
gemini-3.1-flash-image-preview unless user asks for Pro or needs Pro features.
- Aspect ratio:
1:1 (default). Supported: 1:1, 1:4, 1:8, 2:3, 3:2, 3:4, 4:1, 4:3, 4:5, 5:4, 8:1, 9:16, 16:9, 21:9 (1:4/1:8/4:1/8:1 are Nano Banana 2 only).
- Resolution:
1K (default). Supported: 512px, 1K, 2K, 4K; 512px is Nano Banana 2 only.
- Response modalities:
TEXT,IMAGE by default. Use --image-only for image-only output.
- Output format: PNG (default).
- Prefer the bundled CLI over writing new scripts.
- Never modify
scripts/gemini_image_gen.py. Ask the user if something is missing.
Prompt augmentation
Reformat user prompts into a structured spec. Only make implicit details explicit; do not invent new requirements.
Template (include only relevant lines):
Use case: <taxonomy slug>
Primary request: <user's main prompt>
Scene/background: <environment>
Subject: <main subject>
Style/medium: <photo/illustration/3D/etc>
Composition/framing: <wide/close/top-down; placement>
Lighting/mood: <lighting + mood>
Color palette: <palette notes>
Text (verbatim): "<exact text>"
Constraints: <must keep/must avoid>
Avoid: <negative constraints>
Use-case taxonomy
Generate:
- photorealistic-scene -- candid/editorial photos with real texture and natural lighting
- product-mockup -- product/packaging shots, catalog imagery
- ui-mockup -- app/web interface mockups
- infographic-diagram -- diagrams/infographics with structured layout and text
- logo-brand -- logo/mark exploration, minimal flat design
- illustration-sticker -- icons, stickers, kawaii art, children's book art
- stylized-concept -- style-driven concept art, 3D renders
- minimalist-negative-space -- backgrounds for websites/presentations with text overlay space
- search-grounded -- real-time data visualization (weather, charts, events)
Edit:
- style-transfer -- apply reference style to new subject
- object-edit -- remove/replace specific elements
- text-localization -- translate/replace in-image text
- multi-image-composition -- combine multiple reference images
- sketch-to-render -- drawing/line art to photoreal render
Prompting best practices
- Describe the scene narratively; do not just list keywords.
- Use photography language for photorealism (lens, lighting, framing).
- For text in images: quote exact text, specify font style descriptively, use Pro model.
- Nano Banana 2 also supports Thinking and Search Grounding; use it when speed matters.
- For Pro model: let Thinking work; complex prompts get better results.
- For Search grounding: describe what real-time data to visualize.
- For edits, repeat invariants ("change only X; keep Y unchanged").
- Iterate with single-change follow-ups.
Reference map
references/cli.md: how to run generation/edits via scripts/gemini_image_gen.py (commands, flags, recipes).references/gemini-api.md: API-level parameters, models, authentication modes, constraints.references/prompting.md: prompting principles, strategies, and templates from Google's official guide.references/sample-prompts.md: copy/paste prompt recipes for each use-case taxonomy slug.references/vertex-setup.md: how to configure Vertex AI credentials (service account, env vars, IAM roles).
