Nano Banana

Nano Banana (Gemini Image Generation)

Generate and edit images using Google's Gemini native image models. Supports text-to-image, image editing, and multi-image composition via the standard generateContent endpoint.

Official docs: https://ai.google.dev/gemini-api/docs/image-generation

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When to Use

Use this skill when you need to:

• Generate images from text prompts
• Edit an existing image with a text instruction (inpaint / restyle / add-remove)
• Compose multiple input images into one output (e.g. put a product into a scene)
• Iterate on an image conversationally with fine-grained control

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Prerequisites

Connect the Nano Banana connector at app.vm0.ai/connectors. Enabling the connector provisions NANO_BANANA_TOKEN — no Google Cloud account or user-supplied key is required.

Troubleshooting: If requests fail, run zero doctor check-connector --env-name NANO_BANANA_TOKEN or zero doctor check-connector --url https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash-image:generateContent --method POST

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How to Use

All calls hit POST https://generativelanguage.googleapis.com/v1beta/models/<model>:generateContent with header x-goog-api-key: $NANO_BANANA_TOKEN. The output image comes back Base64-encoded in candidates[0].content.parts[*].inline_data.data.

1. Text-to-Image (Flash — fast, cheap default)

Write to /tmp/nano_banana_request.json:

{
  "contents": [
    {
      "parts": [
        { "text": "A golden retriever puppy wearing a tiny chef hat, studio lighting, photorealistic" }
      ]
    }
  ]
}

curl -s -X POST "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash-image:generateContent" --header "x-goog-api-key: $NANO_BANANA_TOKEN" --header "Content-Type: application/json" -d @/tmp/nano_banana_request.json > /tmp/nano_banana_response.json

2. Text-to-Image (Pro — highest quality)

curl -s -X POST "https://generativelanguage.googleapis.com/v1beta/models/gemini-3-pro-image-preview:generateContent" --header "x-goog-api-key: $NANO_BANANA_TOKEN" --header "Content-Type: application/json" -d @/tmp/nano_banana_request.json > /tmp/nano_banana_response.json

3. Extract and Save the Image

The response contains one or more parts; the image part has inline_data.mime_type starting with image/. Extract and decode:

jq -r '.candidates[0].content.parts[] | select(.inline_data != null) | .inline_data.data' /tmp/nano_banana_response.json | base64 -d > /tmp/nano_banana_output.png

4. Edit an Existing Image (Image-to-Image)

Pass the input image as a second part. Use a local file or URL → Base64:

base64 -w0 /path/to/input.jpg > /tmp/nano_banana_input_b64.txt

Write to /tmp/nano_banana_request.json:

{
  "contents": [
    {
      "parts": [
        { "text": "Replace the background with a snowy mountain range at sunset. Keep the subject unchanged." },
        {
          "inline_data": {
            "mime_type": "image/jpeg",
            "data": "<PASTE_CONTENTS_OF_/tmp/nano_banana_input_b64.txt>"
          }
        }
      ]
    }
  ]
}

Or build the JSON with jq to avoid pasting:

jq -n --rawfile img /tmp/nano_banana_input_b64.txt '{
  contents: [{
    parts: [
      { text: "Replace the background with a snowy mountain range at sunset. Keep the subject unchanged." },
      { inline_data: { mime_type: "image/jpeg", data: $img } }
    ]
  }]
}' > /tmp/nano_banana_request.json

curl -s -X POST "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash-image:generateContent" --header "x-goog-api-key: $NANO_BANANA_TOKEN" --header "Content-Type: application/json" -d @/tmp/nano_banana_request.json > /tmp/nano_banana_response.json

5. Multi-Image Composition

Combine multiple input images into one output — e.g. put a product (image A) into a scene (image B):

jq -n \
  --rawfile a /tmp/product_b64.txt \
  --rawfile b /tmp/scene_b64.txt \
  '{
    contents: [{
      parts: [
        { text: "Place the product from the first image onto the wooden table in the second image. Match the lighting and shadows." },
        { inline_data: { mime_type: "image/png", data: $a } },
        { inline_data: { mime_type: "image/jpeg", data: $b } }
      ]
    }]
  }' > /tmp/nano_banana_request.json

curl -s -X POST "https://generativelanguage.googleapis.com/v1beta/models/gemini-3-pro-image-preview:generateContent" --header "x-goog-api-key: $NANO_BANANA_TOKEN" --header "Content-Type: application/json" -d @/tmp/nano_banana_request.json > /tmp/nano_banana_response.json

6. Control Output Modalities and Aspect Ratio

Gemini can return text alongside images. To request image-only output and a specific aspect ratio, add generationConfig:

{
  "contents": [
    { "parts": [{ "text": "A minimalist poster for a jazz festival" }] }
  ],
  "generationConfig": {
    "responseModalities": ["IMAGE"],
    "imageConfig": {
      "aspectRatio": "16:9",
      "imageSize": "2K"
    }
  }
}

curl -s -X POST "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash-image:generateContent" --header "x-goog-api-key: $NANO_BANANA_TOKEN" --header "Content-Type: application/json" -d @/tmp/nano_banana_request.json > /tmp/nano_banana_response.json

7. Conversational Editing (Multi-Turn Refinement)

Continue refining by appending the previous model turn and a new user message. Reuse the Base64 image the model returned so you don't re-upload:

PREV_IMG=$(jq -r '.candidates[0].content.parts[] | select(.inline_data != null) | .inline_data.data' /tmp/nano_banana_response.json)

jq -n --arg img "$PREV_IMG" '{
  contents: [
    { role: "user",  parts: [{ text: "A minimalist poster for a jazz festival" }] },
    { role: "model", parts: [{ inline_data: { mime_type: "image/png", data: $img } }] },
    { role: "user",  parts: [{ text: "Make the typography bolder and shift the palette to deep blue and gold." }] }
  ]
}' > /tmp/nano_banana_request.json

curl -s -X POST "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash-image:generateContent" --header "x-goog-api-key: $NANO_BANANA_TOKEN" --header "Content-Type: application/json" -d @/tmp/nano_banana_request.json > /tmp/nano_banana_response.json

8. Inspect Any Text the Model Returns

The model may include a short text caption/explanation alongside the image:

jq -r '.candidates[0].content.parts[] | select(.text != null) | .text' /tmp/nano_banana_response.json

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Model Reference

Aspect Ratios

1:1, 2:3, 3:2, 3:4, 4:3, 4:5, 5:4, 9:16, 16:9, 21:9, 1:4, 4:1, 1:8, 8:1.

Image Size

generationConfig.imageConfig.imageSize — "512", "1K" (default), "2K", "4K". Larger sizes cost more and are only relevant to final renders; keep iteration at 1K.

Response Shape

{
  "candidates": [{
    "content": {
      "parts": [
        { "text": "Optional caption..." },
        { "inline_data": { "mime_type": "image/png", "data": "<base64>" } }
      ]
    },
    "finishReason": "STOP"
  }]
}

Guidelines

1. Endpoint is per-model — the URL ends with <model>:generateContent. Don't try /v1beta/models:generateContent with a model field in the body; the firewall only allows the per-model endpoints. 2. Use JSON files for request bodies — write to /tmp/nano_banana_*.json to avoid shell quoting issues with long prompts and Base64 payloads. 3. Always `base64 -w0` when preparing Linux image input — base64 without -w0 inserts newlines that break JSON escaping. 4. Output is Base64, never a URL — decode inline_data.data and write bytes directly to disk. The mime_type tells you the extension (png / jpeg / webp). 5. Prefer Flash for iteration, switch to Pro for finals — Flash turns around in a few seconds; Pro is noticeably slower but sharper on text, hands, and fine detail. 6. Keep prompts concrete — describe subject, style, lighting, composition, and mood. For edits, say what to change and what to keep. 7. Input image size — downscale very large inputs before Base64-encoding; the full round-trip cost scales with payload size.