Nano Banana

Nano Banana

AI image generation and editing skill powered by Google Gemini. Generates images from text prompts, edits existing images, removes backgrounds for web-ready transparent PNGs, and performs region-targeted inpainting.

Setup Verification

The agent should verify the following before using this skill:

1. Check Python: python3 --version (requires 3.9+)
2. Check dependencies: python3 -c "from google import genai; print('ok')" — if it fails, install: pip install -r scripts/requirements.txt
3. Check API key: Verify GEMINI_API_KEY is available. Retrieve from your secret manager:

• GCP: gcloud secrets versions access latest --secret=GEMINI_API_KEY
• Or check for a .env / .env.local file in the working directory

1. For inpainting only: Verify Vertex AI auth: gcloud auth application-default print-access-token (inpainting uses Vertex AI, not the Gemini API key)

If the API key is in a secret manager, export it before running scripts:

export GEMINI_API_KEY=$(gcloud secrets versions access latest --secret=GEMINI_API_KEY)

Capabilities

1. Text-to-Image Generation

Generate images from text descriptions.

python3 scripts/generate_image.py "a serene Japanese garden with cherry blossoms" \
  --aspect-ratio 16:9

Parameters:

• prompt (required): Text description of the image to generate
• --output / -o: Output directory or file path (default: current directory)
• --filename / -f: Custom filename (default: auto-generated with timestamp)
• --aspect-ratio / -a: Image aspect ratio (default: 1:1)
• --model / -m: Model to use (default: gemini-2.5-flash-image)

2. Image Editing

Edit existing images with text prompts.

python3 scripts/edit_image.py "change the sky to sunset colors" \
  --images landscape.jpg

# Multiple images for consistency/reference (up to 14):
python3 scripts/edit_image.py "create a collage combining these images" \
  --images img1.png img2.png img3.png --aspect-ratio 16:9

Parameters:

• prompt (required): Text description of the edit
• --images / -i (required): One or more input image paths
• --output / -o: Output directory or file path (default: current directory)
• --filename / -f: Custom filename (default: auto-generated)
• --aspect-ratio / -a: Output aspect ratio (optional)
• --model / -m: Model to use

3. Background Removal (Web-Asset Pipeline)

Remove backgrounds to produce transparent PNGs for websites. This is a three-pass workflow.

CRITICAL: Read references/web-asset-workflow.md before attempting background removal. Agents frequently get this wrong. The key rules:

1. Always use FOUR separate passes — do not try to generate a transparent image in one shot
2. Pass 1 — Generate: Create the image with a solid-color background (white, light gray, or magenta for earth-toned subjects)
3. Pass 2 — Remove background: Use edit_image.py to visually remove the background
4. Pass 3 — Make transparent: Use make_transparent.py to convert to real RGBA (Gemini outputs a checkerboard pattern, NOT actual alpha transparency)
5. Pass 4 — Compress: pngquant --quality=65-85 to get under 500KB for web delivery
6. Output MUST be PNG — JPEG does not support transparency

# Pass 1: Generate the subject (use magenta bg for earth-toned subjects)
python3 scripts/generate_image.py \
  "a modern two-story ADU building, isometric tilt-shift miniature style, on a simple white background" \
  --output ./assets/ --filename subject.png

# Pass 2: Remove the background (Gemini renders checkerboard, not real alpha)
python3 scripts/edit_image.py \
  "Remove the background completely. Make the background fully transparent. Keep only the main subject with clean edges. Output as PNG with alpha transparency." \
  --images ./assets/subject.png \
  --output ./assets/ --filename subject-keyed.png

# Pass 3: Convert checkerboard to real RGBA transparency
python3 scripts/make_transparent.py ./assets/subject-keyed.png \
  --output ./assets/ --filename subject-transparent.png

# Pass 4: Compress for web (target: under 500KB)
pngquant --quality=65-85 --force --output ./assets/subject-transparent.png \
  ./assets/subject-transparent.png

Why Pass 3 is required: Gemini's background removal renders a visible checkerboard pattern to represent transparency, but the actual PNG output is RGB with no alpha channel. The make_transparent.py script detects this checkerboard pattern and converts it to real RGBA transparency with a proper alpha channel.

If artifacts remain (white fringe near shadows), adjust the threshold:

python3 scripts/make_transparent.py ./assets/subject-keyed.png \
  --threshold 210 --feather 2

Naming convention: Web-ready transparent assets should use the -transparent.png suffix.

4. Region-Targeted Inpainting (Vertex AI)

Edit ONLY a specific region of an image while preserving everything else. Uses Imagen 3 via Vertex AI (requires gcloud auth application-default login, not GEMINIAPIKEY).

# Edit a bounding box region (auto-generates mask):
python3 scripts/inpaint_image.py "redraw these arrows with clean routing" \
  --image figure.png --bbox 100,200,500,600

# With a pre-made mask PNG (white=edit, black=preserve):
python3 scripts/inpaint_image.py "fix the overlapping labels" \
  --image figure.png --mask region_mask.png

# Remove content from a region:
python3 scripts/inpaint_image.py "" --image photo.png --bbox 50,50,300,200 --mode remove

Parameters:

• prompt (required): What to draw in the masked region (empty string for removal)
• --image / -i (required): Source image path
• --mask / -m: Path to mask PNG (white=area to edit, black=preserve)
• --bbox / -b: Bounding box x1,y1,x2,y2 — auto-generates a rectangular mask
• --output / -o: Output directory or file path
• --filename / -f: Custom filename
• --mode: insert (default) or remove
• --mask-dilation: Mask edge expansion 0.0-1.0 (default: 0.01)
• --num-images / -n: Generate 1-4 candidates (default: 1)
• --negative-prompt: What to avoid

When to use inpaint vs edit:

• Inpaint — fix a specific area without disturbing the rest (e.g., fix arrows in one corner)
• Edit — the entire image needs modification (e.g., style changes, background removal)

Supported Parameters

Aspect Ratios

1:1 | 2:3 | 3:2 | 3:4 | 4:3 | 4:5 | 5:4 | 9:16 | 16:9 | 21:9

Models

| Model ID | Name | Best For | |----------|------|----------| | gemini-2.5-flash-image | Nano Banana Flash (default) | Fast iterations, lower cost, good quality | | gemini-3-pro-image-preview | Nano Banana Pro | Higher quality, text rendering, complex prompts |

Default is Flash. Use Pro when you need higher fidelity for final assets or when the prompt requires advanced reasoning (text in images, complex compositions).

Web-Asset Rendering Pipeline

For generating assets intended for websites, follow the full pipeline documented in references/web-asset-workflow.md. Summary:

Generate (solid bg) → Remove BG (Gemini) → Make Transparent (real RGBA) → Compress (pngquant) → Integrate

Key integration patterns for web:

• Use object-contain to preserve aspect ratios
• Apply drop-shadow for floating depth on gradient backgrounds
• Use Next.js ` with quality={85} and appropriate sizes`
• For random/deterministic assignment from a pool, hash the entity ID

See examples/web-asset-pipeline.md for a complete walkthrough.

Resources

scripts/

• generate_image.py — Text-to-image generation (Gemini API)
• edit_image.py — Image editing and visual background removal (Gemini API)
• make_transparent.py — Converts checkerboard backgrounds to real RGBA transparency (Pillow + scipy)
• inpaint_image.py — Region-targeted inpainting (Imagen 3, Vertex AI)
• requirements.txt — Python dependencies

references/

• api_reference.md — Model comparison, error codes, rate limits, prompting tips
• web-asset-workflow.md — Multi-pass rendering pipeline for web-ready transparent PNGs

examples/

• web-asset-pipeline.md — Step-by-step example of the full generate → key → integrate workflow

Notes

• All generated images include invisible SynthID watermarks (added by Gemini)
• Pro model supports up to 14 reference images for style/subject consistency
• The background removal pass may need 1-2 attempts — always verify the output
• For batch generation, use Flash model for iterations and Pro for final versions

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: pbc-os
• Source: pbc-os/smb-starter-kit
• License: MIT

Install and usage instructions live in the source repository linked above.