Nano Banana 2 API 使用教程：完整代码示例指南（2026）

pip install google-generativeai

import google.generativeai as genai

# 配置 API 密钥
genai.configure(api_key="YOUR_GOOGLE_API_KEY")

# 初始化模型
model = genai.GenerativeModel("gemini-3.1-flash-image-preview")

# 生成图像
response = model.generate_content(
    "A photorealistic golden retriever puppy in a sunlit meadow",
    generation_config=genai.GenerationConfig(
        response_modalities=["image"]
    )
)

# 保存图像
if response.candidates[0].content.parts:
    image_data = response.candidates[0].content.parts[0].inline_data.data
    with open("output.png", "wb") as f:
        f.write(image_data)
    print("图像已保存为 output.png")

response = model.generate_content(
    "A futuristic cityscape at sunset with flying cars",
    generation_config=genai.GenerationConfig(
        response_modalities=["image"],
        thinking_level="high",  # 复杂提示词使用 'high' 或 'dynamic'
        temperature=0.7,
        top_p=0.95
    )
)

npm install @google/generative-ai

const { GoogleGenerativeAI } = require("@google/generative-ai");
const fs = require("fs");

// 初始化客户端
const genAI = new GoogleGenerativeAI("YOUR_GOOGLE_API_KEY");

async function generateImage() {
  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"]
    }
  });

  // 保存图像
  const imageData = result.response.candidates[0].content.parts[0].inlineData.data;
  fs.writeFileSync("output.png", Buffer.from(imageData, "base64"));
  console.log("图像已保存为 output.png");
}

generateImage();

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_retry(model, prompt, max_retries=3):
    for attempt in range(max_retries):
        try:
            return model.generate_content(prompt)
        except Exception as e:
            if "429" in str(e) and attempt < max_retries - 1:
                wait_time = 2 ** attempt
                print(f"速率限制。等待 {wait_time} 秒...")
                time.sleep(wait_time)
            else:
                raise

# 生成初始图像
chat = model.start_chat()
response1 = chat.send_message(
    "A red sports car in a desert",
    generation_config=genai.GenerationConfig(response_modalities=["image"])
)

# 编辑图像
response2 = chat.send_message(
    "Add mountains in the background",
    generation_config=genai.GenerationConfig(response_modalities=["image"])
)

response = model.generate_content(
    "A cartoon character: a blue robot with a friendly smile. "
    "Show this character in three poses: waving, sitting, jumping",
    generation_config=genai.GenerationConfig(
        response_modalities=["image"],
        thinking_level="high"  # 提高一致性
    )
)

response = model.generate_content(
    "A panoramic landscape, 8:1 aspect ratio, mountain range at dawn",
    generation_config=genai.GenerationConfig(
        response_modalities=["image"]
    )
)

import asyncio

async def generate_batch(prompts):
    tasks = [
        model.generate_content_async(
            prompt,
            generation_config=genai.GenerationConfig(response_modalities=["image"])
        )
        for prompt in prompts
    ]
    return await asyncio.gather(*tasks)

prompts = [
    "A sunset over the ocean",
    "A forest in autumn",
    "A snowy mountain peak"
]

results = asyncio.run(generate_batch(prompts))

# 缓存系统提示词
cached_model = genai.GenerativeModel(
    "gemini-3.1-flash-image-preview",
    system_instruction="Generate images in a consistent art style: watercolor paintings with soft edges"
)

# 重复使用缓存的上下文
response1 = cached_model.generate_content("A cat")
response2 = cached_model.generate_content("A dog")  # 重用缓存的风格指令