Nano Banana

Nano Banana Skill

Generate, edit, and iterate on visual imagery conversationally using Google's native Nano Banana image generation foundation models (gemini-3.1-flash-lite-image, gemini-3.1-flash-image, gemini-3-pro-image, gemini-2.5-flash-image).

Available scripts

• scripts/banana.py: Production CLI tool for text-to-image generation, multimodal editing, and consistency anchoring with model capability validation.
• scripts/test_banana.py: Test suite verifying CLI argument parsing and capability validation guards.

Trigger Conditions

Activate this skill whenever the user asks to:

• Generate new images or illustrations from text prompts.
• Edit, transform, style, or combine existing images.
• Use "banana" as a verb (e.g., "please banana this image", "banana this character into anime style", "banana this photo into chibi style").
• Maintain character, subject, or style consistency across generated imagery using reference images.
• Create 4K high-resolution visual assets or extreme aspect-ratio banners (1:4, 4:1, 1:8, 8:1).

Model Selection & Capability Matrix

Deep Technical References

Consult dedicated reference cards in references/ for full specs, exact token counts, and pixel dimensions:

• references/nano-banana-2-lite.md: Consult when building real-time UI tools, fast prototyping, or cost-critical high-frequency pipelines.
• references/nano-banana-2.md: Consult for 4K generation, Google Image Search Grounding, video-to-image workflows, and ultra-wide/tall banners (1:4, 4:1, 1:8, 8:1).
• references/nano-banana-pro.md: Consult for studio asset production, multi-reference consistency across 14 anchors (objects + characters + artistic style), and storyboards.
• references/nano-banana.md: Consult for gemini-2.5-flash-image specifications and migration paths.
• references/README.md: Index and guidelines for storing project-specific visual consistency anchors.

Core Execution Workflows

1. CLI Execution via scripts/banana.py

Run scripts/banana.py with uv run to generate or edit images:

# High-velocity 1K generation with Nano Banana 2 Lite (Default)
uv run scripts/banana.py \
  -p "A minimalist flat illustration of a coffee cup on a wooden table" \
  -f "coffee_lite.png" \
  -m "nano-banana-2-lite" \
  -a "1:1"

# Studio-quality 4K generation with Nano Banana Pro
uv run scripts/banana.py \
  -p "An authentic architectural photograph of a modern library atrium with skylights" \
  -f "library_4k.png" \
  -m "nano-banana-pro" \
  -r "4K" \
  -a "16:9" \
  --search

# Ultra-wide banner (4:1) with Nano Banana 2 and Image Search Grounding
uv run scripts/banana.py \
  -p "A panorama header of the Swiss Alps at sunrise with fresh snow" \
  -f "alps_banner.png" \
  -m "nano-banana-2" \
  -r "2K" \
  -a "4:1" \
  --image-search

# Conversational Image Editing ("banana this") with Multi-Reference Consistency
uv run scripts/banana.py \
  -p "banana this character: place the character into an astronaut suit on Mars" \
  -i "references/mascot_front.png" \
  -i "references/suit_concept.png" \
  -f "astronaut_mascot.png" \
  -m "nano-banana-2" \
  -r "2K"

CLI Argument Reference

• -p, --prompt: Text prompt describing generation or edit instructions (required).
• -f, --filename: Output file path for generated PNG/JPEG (required).
• -i, --input-image: Path to input/reference image(s). Can be specified up to 14 times.
• -m, --model: nano-banana-2-lite (default), nano-banana-2, nano-banana-pro, nano-banana.
• -r, --resolution: 512px, 1K (default), 2K, 4K.
• -a, --aspect-ratio: 1:1 (default), 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.
• --thinking-level: minimal or high (Banana 2 & Banana 2 Lite).
• --search: Enable Google Search Grounding (Banana 2 & Banana Pro).
• --image-search: Enable Google Image Search Grounding (Banana 2 only).
• --api: interactions (default, Interactions API) or models (generate_content).

2. Dual SDK Integration Patterns

Interactions API (client.interactions.create) — Recommended

Best for multi-turn editing, search grounding, and stateful iteration:

import base64
from google import genai

client = genai.Client()

# Text-to-Image Generation with 4K Resolution & Search Grounding
interaction = client.interactions.create(
    model="gemini-3.1-flash-image",
    input="An infographic chart showing the timeline of space exploration milestones",
    tools=[{"type": "google_search"}],
    generation_config={"thinking_level": "high"},
    response_format={
        "type": "image",
        "aspect_ratio": "16:9",
        "image_size": "4K",
    },
)

if interaction.output_image:
    with open("space_milestones.png", "wb") as f:
        f.write(base64.b64decode(interaction.output_image.data))

Models API (client.models.generate_content)

Direct stateless multimodal generation:

from google import genai
from PIL import Image

client = genai.Client()

img = Image.open("references/product.png")
response = client.models.generate_content(
    model="gemini-3.1-flash-image",
    contents=[img, "Place this product on a sleek marble countertop with soft studio lighting."],
)

for part in response.candidates[0].content.parts:
    if part.inline_data:
        with open("product_studiolit.png", "wb") as f:
            f.write(part.inline_data.data)
        break

Prompting Best Practices

1. Be Hyper-Specific: Define materials, surface textures, lighting setups, and camera angles (three-point softbox, macro lens, shallow depth of field).
2. Context & Intent: State the functional purpose (e-commerce hero banner, editorial illustration, app store icon).
3. Conversational Inpainting: When editing, clearly describe what to modify while instructing to preserve unchanged surroundings ("Change only the sofa to brown vintage leather. Keep all lighting and room decor untouched.").
4. Positive Framing: Describe what should appear instead of using negative constraints ("an empty street with no signs of vehicles" rather than "no cars").