Edición precisa de imágenes impulsada por Google Gemini 3.0 Pro API con salida nativa en 2K/4K. Ofrece modificaciones locales confiables y alta estabilidad a un costo asequible por solicitud.
Pruebe el Generador de Imágenes AI ahora
La API Google Nano Banana Pro Edit (impulsada por el modelo Gemini 3.0 Pro Image) ofrece edición de imágenes con IA rentable y de nivel producción para desarrolladores y equipos creativos. Esta integración premium de API de edición de imágenes Gemini ayuda a transformar visuales existentes en salidas de alta resolución hasta 4K mediante instrucciones en lenguaje natural. El modelo Gemini aporta razonamiento semántico para ediciones complejas, mientras que la API ofrece integración estable para flujos escalables en Best Image AI.
Nota Asegúrate de que tus prompts cumplan las directrices de seguridad de Google. Si ocurre un error, revisa el prompt para detectar contenido restringido, ajústalo e inténtalo de nuevo.
// 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[].);
;
}
(status === ) {
.(pollResultData..);
;
}
( (resolve, ));
}
# 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 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"
# 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)