nano-banana-pro

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Chinese

Nano Banana Pro (Gemini 3 Pro Image)

Nano Banana Pro（Gemini 3 Pro Image）

Generate high-quality images with Google's Gemini 3 Pro Image API.

使用Google的Gemini 3 Pro Image API生成高质量图像。

Overview

概述

Nano Banana Pro is the marketing name for Gemini 3 Pro Image (

gemini-3-pro-image-preview

), Google's state-of-the-art image generation and editing model built on Gemini 3 Pro.

Nano Banana Pro是Gemini 3 Pro Image（

gemini-3-pro-image-preview

）的营销名称，它是Google基于Gemini 3 Pro打造的最先进的图像生成与编辑模型。

Quick Start

快速开始

Get API Key

获取API密钥

Go to Google AI Studio
Click "Get API Key"
Store securely as environment variable

访问Google AI Studio
点击“获取API密钥”
作为环境变量安全存储

Basic Image Generation (Python)

基础图像生成（Python）

python

from google import genai
from google.genai import types

client = genai.Client(api_key="YOUR_GEMINI_API_KEY")

response = client.models.generate_content(
    model="gemini-3-pro-image-preview",
    contents="A serene Japanese garden with cherry blossoms and a koi pond",
    config=types.GenerateContentConfig(
        response_modalities=['TEXT', 'IMAGE']
    )
)

python

from google import genai
from google.genai import types

client = genai.Client(api_key="YOUR_GEMINI_API_KEY")

response = client.models.generate_content(
    model="gemini-3-pro-image-preview",
    contents="A serene Japanese garden with cherry blossoms and a koi pond",
    config=types.GenerateContentConfig(
        response_modalities=['TEXT', 'IMAGE']
    )
)

Process response

for part in response.candidates[0].content.parts: if hasattr(part, 'text'): print(f"Description: {part.text}") elif hasattr(part, 'inline_data'): # Save image image_data = part.inline_data.data # Base64 encoded mime_type = part.inline_data.mime_type # image/png

    import base64
    with open("output.png", "wb") as f:
        f.write(base64.b64decode(image_data))

undefined

    import base64
    with open("output.png", "wb") as f:
        f.write(base64.b64decode(image_data))

undefined

REST API (cURL)

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": [{
      "role": "user",
      "parts": [{"text": "Create a vibrant infographic about photosynthesis"}]
    }],
    "generationConfig": {
      "responseModalities": ["TEXT", "IMAGE"]
    }
  }'

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": [{
      "role": "user",
      "parts": [{"text": "Create a vibrant infographic about photosynthesis"}]
    }],
    "generationConfig": {
      "responseModalities": ["TEXT", "IMAGE"]
    }
  }'

TypeScript/JavaScript

typescript

const GEMINI_API_KEY = process.env.GEMINI_API_KEY;

async function generateImage(prompt: string) {
  const response = await fetch(
    'https://generativelanguage.googleapis.com/v1beta/models/gemini-3-pro-image-preview:generateContent',
    {
      method: 'POST',
      headers: {
        'x-goog-api-key': GEMINI_API_KEY!,
        'Content-Type': 'application/json',
      },
      body: JSON.stringify({
        contents: [{ 
          role: 'user', 
          parts: [{ text: prompt }] 
        }],
        generationConfig: {
          responseModalities: ['TEXT', 'IMAGE'],
        },
      }),
    }
  );

  const data = await response.json();
  return data;
}

typescript

const GEMINI_API_KEY = process.env.GEMINI_API_KEY;

async function generateImage(prompt: string) {
  const response = await fetch(
    'https://generativelanguage.googleapis.com/v1beta/models/gemini-3-pro-image-preview:generateContent',
    {
      method: 'POST',
      headers: {
        'x-goog-api-key': GEMINI_API_KEY!,
        'Content-Type': 'application/json',
      },
      body: JSON.stringify({
        contents: [{ 
          role: 'user', 
          parts: [{ text: prompt }] 
        }],
        generationConfig: {
          responseModalities: ['TEXT', 'IMAGE'],
        },
      }),
    }
  );

  const data = await response.json();
  return data;
}

Configuration Options

配置选项

Image Configuration

图像配置

python

response = client.models.generate_content(
    model="gemini-3-pro-image-preview",
    contents="Professional product photo of a coffee mug",
    config=types.GenerateContentConfig(
        response_modalities=['TEXT', 'IMAGE'],
        image_config=types.ImageConfig(
            aspect_ratio="16:9",  # Options: 1:1, 3:2, 16:9, 9:16, 21:9
            image_size="2K"       # Options: 1K, 2K, 4K
        )
    )
)

python

response = client.models.generate_content(
    model="gemini-3-pro-image-preview",
    contents="Professional product photo of a coffee mug",
    config=types.GenerateContentConfig(
        response_modalities=['TEXT', 'IMAGE'],
        image_config=types.ImageConfig(
            aspect_ratio="16:9",  # Options: 1:1, 3:2, 16:9, 9:16, 21:9
            image_size="2K"       # Options: 1K, 2K, 4K
        )
    )
)

With Google Search Grounding

结合Google搜索 grounding

python

response = client.models.generate_content(
    model="gemini-3-pro-image-preview",
    contents="Create an infographic showing today's stock market trends",
    config=types.GenerateContentConfig(
        response_modalities=['TEXT', 'IMAGE'],
        tools=[{"google_search": {}}]  # Enable search grounding
    )
)

python

response = client.models.generate_content(
    model="gemini-3-pro-image-preview",
    contents="Create an infographic showing today's stock market trends",
    config=types.GenerateContentConfig(
        response_modalities=['TEXT', 'IMAGE'],
        tools=[{"google_search": {}}]  # Enable search grounding
    )
)

Multi-Turn Conversations (Iterative Editing)

多轮对话（迭代编辑）

python

undefined

python

undefined

Create a chat session

chat = client.chats.create( model="gemini-3-pro-image-preview", config=types.GenerateContentConfig( response_modalities=['TEXT', 'IMAGE'], tools=[{"google_search": {}}] ) )

Initial generation

response1 = chat.send_message( "Create a vibrant infographic explaining photosynthesis" )

Edit the image

response2 = chat.send_message( "Update this infographic to be in Spanish. Keep all other elements the same." )

undefined

response2 = chat.send_message( "Update this infographic to be in Spanish. Keep all other elements the same." )

undefined

Key Capabilities

核心功能

1. Superior Text Rendering

1. 卓越的文本渲染

python

response = client.models.generate_content(
    model="gemini-3-pro-image-preview",
    contents="""Create a professional poster with:
    - Title: "Annual Tech Summit 2025"
    - Date: March 15-17, 2025
    - Location: San Francisco Convention Center
    """,
    config=types.GenerateContentConfig(
        response_modalities=['TEXT', 'IMAGE']
    )
)

python

response = client.models.generate_content(
    model="gemini-3-pro-image-preview",
    contents="""Create a professional poster with:
    - Title: "Annual Tech Summit 2025"
    - Date: March 15-17, 2025
    - Location: San Francisco Convention Center
    """,
    config=types.GenerateContentConfig(
        response_modalities=['TEXT', 'IMAGE']
    )
)

2. Character Consistency (Up to 5 Subjects)

2. 角色一致性（最多5个主体）

python

import base64

def load_image(path: str) -> str:
    with open(path, "rb") as f:
        return base64.b64encode(f.read()).decode()

character_ref = load_image("character.png")

response = client.models.generate_content(
    model="gemini-3-pro-image-preview",
    contents=[
        {"text": "Generate an image of this person at a tech conference"},
        {"inline_data": {"mime_type": "image/png", "data": character_ref}}
    ],
    config=types.GenerateContentConfig(
        response_modalities=['TEXT', 'IMAGE']
    )
)

python

import base64

def load_image(path: str) -> str:
    with open(path, "rb") as f:
        return base64.b64encode(f.read()).decode()

character_ref = load_image("character.png")

response = client.models.generate_content(
    model="gemini-3-pro-image-preview",
    contents=[
        {"text": "Generate an image of this person at a tech conference"},
        {"inline_data": {"mime_type": "image/png", "data": character_ref}}
    ],
    config=types.GenerateContentConfig(
        response_modalities=['TEXT', 'IMAGE']
    )
)

Next.js API Route

Next.js API路由

typescript

// app/api/generate-image/route.ts
import { NextRequest, NextResponse } from 'next/server';

export async function POST(request: NextRequest) {
  const { prompt, aspectRatio = '1:1', imageSize = '2K' } = await request.json();

  try {
    const response = await fetch(
      'https://generativelanguage.googleapis.com/v1beta/models/gemini-3-pro-image-preview:generateContent',
      {
        method: 'POST',
        headers: {
          'x-goog-api-key': process.env.GEMINI_API_KEY!,
          'Content-Type': 'application/json',
        },
        body: JSON.stringify({
          contents: [{ role: 'user', parts: [{ text: prompt }] }],
          generationConfig: {
            responseModalities: ['TEXT', 'IMAGE'],
            imageConfig: { aspectRatio, imageSize },
          },
        }),
      }
    );

    const data = await response.json();
    const parts = data.candidates?.[0]?.content?.parts || [];
    const imagePart = parts.find((p: any) => p.inline_data);

    return NextResponse.json({
      image: imagePart ? {
        data: imagePart.inline_data.data,
        mimeType: imagePart.inline_data.mime_type,
        url: `data:${imagePart.inline_data.mime_type};base64,${imagePart.inline_data.data}`,
      } : null,
    });
  } catch (error) {
    return NextResponse.json({ error: 'Generation failed' }, { status: 500 });
  }
}

typescript

// app/api/generate-image/route.ts
import { NextRequest, NextResponse } from 'next/server';

export async function POST(request: NextRequest) {
  const { prompt, aspectRatio = '1:1', imageSize = '2K' } = await request.json();

  try {
    const response = await fetch(
      'https://generativelanguage.googleapis.com/v1beta/models/gemini-3-pro-image-preview:generateContent',
      {
        method: 'POST',
        headers: {
          'x-goog-api-key': process.env.GEMINI_API_KEY!,
          'Content-Type': 'application/json',
        },
        body: JSON.stringify({
          contents: [{ role: 'user', parts: [{ text: prompt }] }],
          generationConfig: {
            responseModalities: ['TEXT', 'IMAGE'],
            imageConfig: { aspectRatio, imageSize },
          },
        }),
      }
    );

    const data = await response.json();
    const parts = data.candidates?.[0]?.content?.parts || [];
    const imagePart = parts.find((p: any) => p.inline_data);

    return NextResponse.json({
      image: imagePart ? {
        data: imagePart.inline_data.data,
        mimeType: imagePart.inline_data.mime_type,
        url: `data:${imagePart.inline_data.mime_type};base64,${imagePart.inline_data.data}`,
      } : null,
    });
  } catch (error) {
    return NextResponse.json({ error: 'Generation failed' }, { status: 500 });
  }
}

Model Comparison

模型对比

Feature	Nano Banana (2.5 Flash)	Nano Banana Pro (3 Pro Image)
Model ID	gemini-2.5-flash-image	gemini-3-pro-image-preview
Quality	Good	Best
Speed	Faster	Slower
Cost	Lower	Higher
Best For	Previews, high-volume	Production, professional

功能	Nano Banana（2.5 Flash）	Nano Banana Pro（3 Pro Image）
模型ID	gemini-2.5-flash-image	gemini-3-pro-image-preview
图像质量	良好	最佳
生成速度	更快	较慢
成本	更低	更高
适用场景	预览场景、高吞吐量	生产环境、专业场景

Resources

资源

Documentation: https://ai.google.dev/gemini-api/docs/image-generation
Google AI Studio: https://aistudio.google.com
Prompt Guide: https://ai.google.dev/gemini-api/docs/prompting-intro

文档: https://ai.google.dev/gemini-api/docs/image-generation
Google AI Studio: https://aistudio.google.com
提示词指南: https://ai.google.dev/gemini-api/docs/prompting-intro