Nano Banana 2 APIの使い方：コード例付き完全チュートリアル（2026年版）

pip install google-generativeai pillow

import google.generativeai as genai
import base64
from PIL import Image
from io import BytesIO

# APIキーを設定
genai.configure(api_key="YOUR_GOOGLE_API_KEY")

# モデルを初期化
model = genai.GenerativeModel("gemini-3.1-flash-image-preview")

# 画像を生成
response = model.generate_content(
    "A serene Japanese zen garden with cherry blossoms",
    generation_config={
        "response_modalities": ["image"],
        "temperature": 1.0
    }
)

# 画像を保存
if response.candidates[0].content.parts:
    image_data = response.candidates[0].content.parts[0].inline_data.data
    image = Image.open(BytesIO(base64.b64decode(image_data)))
    image.save("zen_garden.png")
    print("画像を保存しました: zen_garden.png")

response = model.generate_content(
    "Ultra-detailed cyberpunk cityscape at night, neon lights, rain",
    generation_config={
        "response_modalities": ["image"],
        "temperature": 1.2,           # 創造性（0.0-2.0）
        "top_p": 0.95,                # 多様性制御
        "top_k": 40,                  # サンプリング幅
        "candidate_count": 1,         # 生成する画像数
        "max_output_tokens": 8192     # 4K画像の場合
    }
)

import time

def generate_with_retry(prompt, max_retries=3):
    for attempt in range(max_retries):
        try:
            response = model.generate_content(
                prompt,
                generation_config={"response_modalities": ["image"]}
            )
            return response
        except Exception as e:
            if "429" in str(e):  # レート制限
                wait_time = 2 ** attempt
                print(f"レート制限。{wait_time}秒待機中...")
                time.sleep(wait_time)
            elif "quota" in str(e).lower():
                print("クォータ超過。有料APIキーを確認してください。")
                break
            else:
                print(f"エラー: {e}")
                break
    return None

npm install @google/generative-ai

import { GoogleGenerativeAI } from "@google/generative-ai";
import fs from "fs";

const genAI = new GoogleGenerativeAI("YOUR_GOOGLE_API_KEY");

async function generateImage(prompt) {
  const model = genAI.getGenerativeModel({
    model: "gemini-3.1-flash-image-preview"
  });

  const result = await model.generateContent({
    contents: [{ role: "user", parts: [{ text: prompt }] }],
    generationConfig: {
      responseModalities: ["image"],
      temperature: 1.0
    }
  });

  const response = await result.response;
  const imageData = response.candidates[0].content.parts[0].inlineData.data;

  // Base64をバッファに変換して保存
  const buffer = Buffer.from(imageData, "base64");
  fs.writeFileSync("output.png", buffer);

  console.log("画像を保存しました: output.png");
}

generateImage("A majestic mountain landscape at sunset");

async function batchGenerate(prompts) {
  const model = genAI.getGenerativeModel({
    model: "gemini-3.1-flash-image-preview"
  });

  for (let i = 0; i < prompts.length; i++) {
    try {
      const result = await model.generateContent({
        contents: [{ role: "user", parts: [{ text: prompts[i] }] }],
        generationConfig: { responseModalities: ["image"] }
      });

      const imageData = result.response.candidates[0].content.parts[0].inlineData.data;
      fs.writeFileSync(`image_${i + 1}.png`, Buffer.from(imageData, "base64"));

      console.log(`画像 ${i + 1}/${prompts.length} 完了`);

      // レート制限を避けるため
      await new Promise(resolve => setTimeout(resolve, 1000));
    } catch (error) {
      console.error(`プロンプト ${i + 1} でエラー:`, error.message);
    }
  }
}

const prompts = [
  "A futuristic robot in a neon city",
  "An ancient temple in a misty forest",
  "A colorful abstract geometric pattern"
];

batchGenerate(prompts);

pip install openai

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_EVOLINK_API_KEY",
    base_url="https://api.evolink.ai/v1",
)

response = client.chat.completions.create(
    model="gemini-3.1-flash-image-preview",
    messages=[
        {
            "role": "user",
            "content": "A photorealistic golden retriever puppy sitting in a sunlit meadow",
        }
    ],
)

print(response.choices[0].message.content)

npm install openai

const OpenAI = require("openai");

const client = new OpenAI({
  apiKey: "YOUR_EVOLINK_API_KEY",
  baseURL: "https://api.evolink.ai/v1",
});

async function generateImage() {
  const response = await client.chat.completions.create({
    model: "gemini-3.1-flash-image-preview",
    messages: [
      {
        role: "user",
        content:
          "A photorealistic golden retriever puppy sitting in a sunlit meadow",
      },
    ],
  });

  console.log(response.choices[0].message.content);
}

generateImage();

import time

def generate_with_backoff(prompt, max_retries=5):
    for i in range(max_retries):
        try:
            return model.generate_content(prompt)
        except Exception as e:
            if "429" in str(e):
                wait = (2 ** i) + random.uniform(0, 1)
                time.sleep(wait)
            else:
                raise

import os
print(os.getcwd())  # 現在のディレクトリを確認

import path from "path";
console.log(process.cwd());