Image Generator

Detailed Guide

Read the detailed guide before executing this skill. It retains the complete procedure and reference material. Treat its safety, prerequisites, and validation requirements as mandatory. For focused work, load the relevant sections; for end-to-end work, read the guide completely.

When to Use

Use when this workflow matches the user request: Generate and edit images using Gemini's Nano Banana Pro model (gemini-3-pro-image-preview). Use this skill when the user asks you to generate images, create visuals, edit photos, create logos, generate product mockups, or perform any image generation/editing task.

_Source: dair-ai/dair-academy-plugins (MIT)._

This skill generates and edits images using Google's Gemini Nano Banana Pro model (gemini-3-pro-image-preview).

API Usage

Basic Text-to-Image (Python)

python
from google import genai
from google.genai import types

client = genai.Client()

response = client.models.generate_content(
    model="gemini-3-pro-image-preview",
    contents=["Your prompt here"],
    config=types.GenerateContentConfig(
        response_modalities=['TEXT', 'IMAGE'],
        image_config=types.ImageConfig(
            aspect_ratio="16:9",  # Optional
            image_size="2K"       # Optional: "1K", "2K", "4K"
        )
    )
)

for part in response.parts:
    if part.text is not None:
        print(part.text)
    elif part.inline_data is not None:
        image = part.as_image()
        image.save("generated_image.png")

Basic Text-to-Image (JavaScript)

javascript
import { GoogleGenAI } from "@google/genai";
import * as fs from "node:fs";

const ai = new GoogleGenAI({});

const response = await ai.models.generateContent({
    model: "gemini-3-pro-image-preview",
    contents: "Your prompt here",
    config: {
        responseModalities: ['TEXT', 'IMAGE'],
        imageConfig: {
            aspectRatio: "16:9",
            imageSize: "2K"
        }
    }
});

for (const part of response.candidates[0].content.parts) {
    if (part.text) {
        console.log(part.text);
    } else if (part.inlineData) {
        const buffer = Buffer.from(part.inlineData.data, "base64");
        fs.writeFileSync("generated_image.png", buffer);
    }
}

REST API (curl)

bash
curl -s -X POST \
  "https://generativelanguage.googleapis.com/v1beta/models/gemini-3-pro-image-preview:generateContent" \
  -H "x-goog-api-key: $GEMINI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "contents": [{
      "parts": [{"text": "Your prompt here"}]
    }],
    "generationConfig": {
      "responseModalities": ["TEXT", "IMAGE"],
      "imageConfig": {
        "aspectRatio": "16:9",
        "imageSize": "2K"
      }
    }
  }' | jq -r '.candidates[0].content.parts[] | select(.inlineData) | .inlineData.data' | base64 --decode > output.png

Image Editing (with input image)

python
from google import genai
from google.genai import types
from PIL import Image

client = genai.Client()

input_image = Image.open('input.png')
prompt = "Add a wizard hat to the cat in this image"

response = client.models.generate_content(
    model="gemini-3-pro-image-preview",
    contents=[prompt, input_image],
    config=types.GenerateContentConfig(
        response_modalities=['TEXT', 'IMAGE']
    )
)

for part in response.parts:
    if part.inline_data is not None:
        image = part.as_image()
        image.save("edited_image.png")

Multi-Image Composition

python
from google import genai
from google.genai import types
from PIL import Image

client = genai.Client()

image1 = Image.open('dress.png')
image2 = Image.open('model.png')
prompt = "Put the dress from the first image on the model from the second image"

response = client.models.generate_content(
    model="gemini-3-pro-image-preview",
    contents=[image1, image2, prompt],
    config=types.GenerateContentConfig(
        response_modalities=['TEXT', 'IMAGE'],
        image_config=types.ImageConfig(
            aspect_ratio="3:4",
            image_size="2K"
        )
    )
)

With Google Search Grounding

python
from google import genai
from google.genai import types

client = genai.Client()

response = client.models.generate_content(
    model="gemini-3-pro-image-preview",
    contents="Visualize the current weather forecast for San Francisco",
    config=types.GenerateContentConfig(
        response_modalities=['TEXT', 'IMAGE'],
        image_config=types.ImageConfig(aspect_ratio="16:9"),
        tools=[{"google_search": {}}]
    )
)

Limitations

• Requires the upstream tool, account, API key, or local setup when the workflow names one.
• Does not authorize destructive, production, paid, or external-message actions without explicit user approval.
• Validate generated artifacts or recommendations against the user's real sources before treating them as final.