
文字轉圖片
從文字提示詞建立 AI 圖片
基於 Google Gemini 3.0 Pro 的精確圖像編輯 API,支援原生 2K/4K 超高畫質編輯輸出,每一次修圖都能實現細膩還原。API 提供可靠的局部修改能力和超強穩定性,支援精細化編輯操作與自動批量處理,以實惠低廉的單次請求成本服務開發者和大型項目。適合產品圖修正、背景替換、細節優化等專業編輯需求,能高效整合到自動化編輯、後期修圖、內容標準化等批量處理管道,節省時間提升產出質量。
立即體驗 AI 圖片生成器
| 參數 | 價格 | 原價 | 折扣 |
|---|
Google Nano Banana Pro Edit API(由 Gemini 3.0 Pro Image 模型驅動)為開發者與創意團隊提供經濟高效、可投入生產的 AI 圖像編輯能力。這項高階 Gemini 圖像編輯 API 整合,能協助您透過自然語言指令將既有視覺素材轉換為最高 4K 的高解析度輸出。Gemini 模型為複雜編輯提供語意推理,而 API 則為 Flaq AI 上的可擴展工作流程提供穩定整合。
注意 請確保您的提示詞符合 Google 的安全指南。如果發生錯誤,請檢查您的提示詞是否包含受限內容,調整後再試一次。
在瀏覽器中探索多種 AI 創作工具,快速完成圖片與影片工作流程,然後透過 Flaq AI 的生產級模型 API 擴展成功想法。Flaq AI 為所有模型提供統一 API 層,讓你的工作流程更容易使用和擴展。
// Step 1: Submit generation request
// width and height must be aspect-ratio integers such as 16 and 9,
// not pixel dimensions like 768 and 1280.
// Currently all width/height values are passed as ratio integers,
// and the backend does not support custom pixel dimensions for these fields.
const response = await fetch('https://api.flaq.ai/api/v1/image/task', {
method: 'POST',
headers: {
'Content-Type': 'application/json',
'Authorization': 'Bearer YOUR_API_KEY'
},
body: JSON.stringify({
model_name: 'nano-banana-pro-edit',
prompt: 'Transform this image into a watercolor painting style',
width: 16, // Aspect-ratio value, not pixel width
height: 9, // Aspect-ratio value, not pixel height
resolution: '2k', // Supported values: '1k', '2k', '4k'
image_url_list: [
'https://example.com/image1.jpg',
'https://example.com/image2.jpg'
]
})
});
const { data } = await response.json();
const taskId = data.task_id;
// Step 2: Poll for results
const taskId = data.task_id;
const pollResult = async (taskId) => {
const res = await fetch(`https://api.flaq.ai/api/v1/image/${taskId}`, {
headers: { 'Authorization': 'Bearer YOUR_API_KEY' }
});
return res.json();
};
while (true) {
const pollResultData = await pollResult(taskId);
const status = pollResultData.data.task_status;
if (status === 'succeed') {
console.log(pollResultData.data.task_result.images[0].url);
break;
}
if (status === 'failed') {
console.error(pollResultData.data.task_status_msg);
break;
}
await new Promise(resolve => setTimeout(resolve, 10000));
}
# Step 1: Submit generation request
import requests
response = requests.post(
'https://api.flaq.ai/api/v1/image/task',
headers={
'Content-Type': 'application/json',
'Authorization': 'Bearer YOUR_API_KEY'
},
json={
'model_name': 'nano-banana-pro-edit',
'prompt': 'Transform this image into a watercolor painting style',
'width': 16, # Aspect-ratio value, not pixel width
'height': 9, # Aspect-ratio value, not pixel height
'resolution': '2k', # Supported values: '1k', '2k', '4k'
'image_url_list': [
'https://example.com/image1.jpg',
'https://example.com/image2.jpg'
]
}
)
result = response.json()
task_id = result['data']['task_id']
# Step 2: Poll for results
task_id = response.json()['data']['task_id']
poll_url = f"https://api.flaq.ai/api/v1/image/{task_id}"
while True:
poll_result = requests.get(poll_url, headers={'Authorization': 'Bearer YOUR_API_KEY'}).json()
status = poll_result['data']['task_status']
if status == 'succeed':
print(poll_result['data']['task_result']['images'][0]['url'])
break
if status == 'failed':
print(poll_result['data']['task_status_msg'])
break
time.sleep(10)
# Step 1: Submit generation request
curl -X POST https://api.flaq.ai/api/v1/image/task \
-H "Content-Type: application/json" \
-H "Authorization: Bearer YOUR_API_KEY" \
-d '{
"model_name": "nano-banana-pro-edit",
"prompt": "Transform this image into a watercolor painting style",
"width": 16,
"height": 9,
"resolution": "2k",
"image_url_list": [
"https://example.com/image1.jpg",
"https://example.com/image2.jpg"
]
}'
# Step 2: Poll for results
# Replace {task_id} with the task_id returned from the submit response
curl -X GET "https://api.flaq.ai/api/v1/image/{task_id}" \
-H "Authorization: Bearer YOUR_API_KEY"