使用方法
- 复制安装命令，在自己的智能体环境安装前先检查源码包。
- 从原始文档核对所需工具、凭据和支持的宿主。
- 说明输入材料与期望交付物，并对照来源检查输出。
npx skills add https://github.com/hoodini/ai-agents-skills --skill nano-banana-pro开始前需了解
- 安装不包含模型订阅、API 额度或访问文件的权限。
- 仓库星数和安装量反映活跃情况，不等于独立质量或安全测试。
仓库原始内容
按源仓库内容呈现，保留标题、案例、代码、表格、链接以及原文引用的演示图片。
Nano Banana Pro (Gemini 3 Pro Image)
Generate high-quality images with Google's Gemini 3 Pro Image API.
Overview
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.
Quick Start
Get API Key
- Go to Google AI Studio
- Click "Get API Key"
- Store securely as environment variable
Basic Image Generation (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']
)
)
# 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))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"]
}
}'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;
}Configuration Options
Image Configuration
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
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)
python
# 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."
)Key Capabilities
1. Superior Text Rendering
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)
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
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
| 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 |
Resources
- 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
