
文本转图像
从文本提示词创建 AI 图像
Google Gemini 3.1 Flash 快速稳定的图像编辑 API,编辑响应迅速,轻松承载高并发调用。API 调用成本极低,服务稳定可靠,专为背景替换、色彩调整等轻量级编辑需求打造。非常适合需要可靠性和极低成本 API 入口的实时应用、用户交互式编辑工具和高频编辑场景。灵活扩展,高效降本,全面助力高频内容创新,满足多层次企业需求。
立即体验 AI 图片生成
| 参数 | 价格 | 原价 | 折扣 |
|---|
Google Nano Banana 2 Edit API(由 Gemini 3.1 Flash Image 模型驱动)为开发者和创意团队提供经济高效、生产级的 AI 图像编辑能力。这个经济实惠的 Gemini 图像编辑 API 集成帮助您通过自然语言指令将现有视觉内容转换为专业输出。Gemini 模型为复杂编辑提供语义推理能力,而 API 则为 Flaq AI 上的可扩展工作流提供稳定集成。
注意 请确保您的提示符合 Google 的安全指南。如果发生错误,请检查您的提示是否包含受限内容,调整后重试。
在浏览器中探索多种 AI 创作工具,用于快速图像和视频工作流,然后通过 Flaq AI 可用于生产的模型 API 扩展成功创意。Flaq AI 为所有模型提供统一 API 层,让你的工作流更容易使用和扩展。
// Step 1: Submit generation request
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-2-edit',
prompt: 'Add flying cars and neon lights to this cityscape',
image_url_list: ['https://example.com/input-image.jpg'],
width: 16,
height: 9,
resolution: '2k'
})
});
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-2-edit',
'prompt': 'Add flying cars and neon lights to this cityscape',
'image_url_list': ['https://example.com/input-image.jpg'],
'width': 16,
'height': 9,
'resolution': '2k'
}
)
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-2-edit",
"prompt": "Add flying cars and neon lights to this cityscape",
"image_url_list": ["https://example.com/input-image.jpg"],
"width": 16,
"height": 9,
"resolution": "2k"
}'
# 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"