Nano Banana 2 API 사용법: 코드 예제가 포함된 완전 가이드 (2026)

pip install google-generativeai

import google.generativeai as genai
import base64
from pathlib import Path

# API 키 설정
genai.configure(api_key="YOUR_GOOGLE_API_KEY")

# 모델 초기화
model = genai.GenerativeModel(
    model_name="gemini-3.1-flash-image-preview"
)

# 이미지 생성
response = model.generate_content(
    "A photorealistic golden retriever puppy in a sunlit meadow",
    generation_config={
        "response_modalities": ["image"],  # 필수
        "temperature": 1.0,
        "top_p": 0.95
    }
)

# 이미지 저장
if response.candidates and response.candidates[0].content.parts:
    for part in response.candidates[0].content.parts:
        if hasattr(part, 'inline_data'):
            image_data = part.inline_data.data
            Path("output.png").write_bytes(base64.b64decode(image_data))
            print("✓ 이미지가 output.png에 저장되었습니다")

npm install @google/generative-ai

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

const genAI = new GoogleGenerativeAI("YOUR_GOOGLE_API_KEY");

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

const result = await model.generateContent({
  contents: [{
    role: "user",
    parts: [{
      text: "A photorealistic golden retriever puppy in a sunlit meadow"
    }]
  }],
  generationConfig: {
    responseModalities: ["image"],
    temperature: 1.0,
    topP: 0.95
  }
});

// 이미지 저장
const response = result.response;
if (response.candidates?.[0]?.content?.parts) {
  for (const part of response.candidates[0].content.parts) {
    if (part.inlineData) {
      const buffer = Buffer.from(part.inlineData.data, "base64");
      fs.writeFileSync("output.png", buffer);
      console.log("✓ 이미지가 output.png에 저장되었습니다");
    }
  }
}

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();

generation_config={
    "response_modalities": ["image"],
    "image_size": "4K"  # 옵션: "512", "1K", "2K", "4K"
}

generation_config={
    "response_modalities": ["image"],
    "aspect_ratio": "16:9"  # 옵션: "1:1", "16:9", "9:16", "4:3", "3:4", "4:1", "1:4", "8:1", "1:8"
}

generation_config={
    "response_modalities": ["image"],
    "thinking_level": "high"  # 옵션: "minimal" (기본값), "high", "dynamic"
}

# 첫 번째 생성
chat = model.start_chat()
response1 = chat.send_message(
    "A red apple on a wooden table",
    generation_config={"response_modalities": ["image"]}
)

# 편집
response2 = chat.send_message(
    "Add a green leaf on top of the apple",
    generation_config={"response_modalities": ["image"]}
)

import time

def generate_with_retry(prompt, max_retries=3):
    for attempt in range(max_retries):
        try:
            return model.generate_content(prompt, generation_config={...})
        except Exception as e:
            if "429" in str(e) and attempt < max_retries - 1:
                wait_time = 2 ** attempt
                print(f"{wait_time}초 후 재시도 중...")
                time.sleep(wait_time)
            else:
                raise