
文本转图像
从文本提示词创建 AI 图像
基于 Google Gemini 3.0 Pro 的精确图像编辑 API,支持原生 2K/4K 输出。API 提供可靠的局部修改能力和高稳定性,支持精细化编辑操作,以实惠的单次请求成本服务广大开发者和专业内容团队。适合产品图修正、背景替换、细节优化等多种专业编辑需求,可轻松集成到自动化编辑工作流和批量处理管道,在长时间高压场景下依然稳定高效,为企业内容优化极致降本增效。提升效率,兼顾品质与预算!
立即体验 AI 图片生成
| 参数 | 价格 | 原价 | 折扣 |
|---|
Google Nano Banana Pro Edit API(由 Gemini 3.0 Pro Image 模型驱动)为开发者和创意团队提供经济高效的生产级 AI 图像编辑服务。这个高级 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最高 4K 的高分辨率输出。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的集成。
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
// 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"