Nano Banana Image Pipeline

A production wrapper around Google's Gemini image models — gemini-2.5-flash-image (Nano Banana) and Nano Banana Pro (Gemini 3 Pro Image) — driven through the google-genai SDK. It handles the full loop of generating, editing, fusing, and refining images, including the parts most people get wrong: parsing inline_data parts out of the response, setting aspect ratio and resolution correctly, and keeping a conversation alive for iterative edits. Built for developers who want reliable image output from code, not a chat UI.

When to use it

• Generating product shots, hero images, or social assets from a text prompt at a fixed aspect ratio.
• Producing infographics, posters, or diagrams where the rendered text must be legible and spelled correctly — Nano Banana Pro is markedly stronger here than typical diffusion models.
• Editing an existing image conversationally ("now make the sky overcast", "remove the person on the left") without re-uploading each turn.
• Fusing 2+ source images — e.g. dropping a product onto a new background, or blending a style reference with a subject.

How it works

Calls go through client.models.generate_content(model=..., contents=[...], config=...). Prompts and source images are passed as contents; images are sent as types.Part.from_bytes(data=..., mime_type="image/png"). Output images come back as candidate parts with inline_data — the skill walks response.candidates[0].content.parts, picks the parts where part.inline_data is set, and writes the bytes out.

Aspect ratio and resolution are passed via image_config inside GenerateContentConfig (e.g. aspect_ratio="16:9"). For multi-turn editing it reuses client.chats.create(...) so prior images stay in context.

Usage

curl -fsSL --create-dirs -o ~/.claude/skills/nano-banana-gemini-image-pipeline/SKILL.md https://claudeskil.com/skills/nano-banana-gemini-image-pipeline/SKILL.md
export GEMINI_API_KEY=...   # from Google AI Studio

from google import genai
from google.genai import types

client = genai.Client()
resp = client.models.generate_content(
    model="gemini-2.5-flash-image",
    contents=["A launch-day infographic: 'Ship Faster', 3 numbered steps"],
    config=types.GenerateContentConfig(
        response_modalities=["IMAGE"],
        image_config=types.ImageConfig(aspect_ratio="4:5"),
    ),
)
for part in resp.candidates[0].content.parts:
    if part.inline_data:
        open("out.png", "wb").write(part.inline_data.data)

Tips & gotchas

• A response can contain both a text part and an image part — always filter on inline_data rather than assuming parts[0] is the image.
• For crisp text and infographics use Nano Banana Pro; gemini-2.5-flash-image is cheaper and faster but weaker on dense typography.
• All Gemini-generated images carry an invisible SynthID watermark — don't claim outputs are watermark-free.
• If you only ask for ["TEXT"] modality you'll get no image; set response_modalities to include "IMAGE".

FAQ

Flash vs. Pro — which? Use Flash for volume and speed; switch to Pro when text rendering, fine detail, or multi-image fusion fidelity matters.

Can I keep editing one image? Yes — use a chats session and send follow-up instructions; the prior image stays in context.

Why is my output blocked or empty? Check the candidate's finish_reason and any safety blocks before reading parts; a filtered response has no image bytes.