# Nano Banana Pro (nano-banana-pro)

Vendor: Google
Model ID: `nano-banana-pro`
Base URL: `https://api.mulerouter.ai`
Type: Inference API (async task-based)

## Description
Versatile edit model combining image-to-image transformation and inpainting capabilities

## Variant: Image to Image Edit

Endpoint: `POST /vendors/google/v1/nano-banana-pro/edit`

### Input Schema
The API accepts the following input parameters:

- **`images`** (`list<string>`, _required_):
  List of images to edit. Supports both image URLs and base64-encoded images (minimum 1, maximum 14). Base64 encoded image data is like "data:image/png;base64,iVBORw0KGgoAAAA..."
  - Items: min: 1, max: 10

- **`prompt`** (`string`, _required_):
  Text prompt for image editing

- **`resolution`** (`string`, _optional_):
  Output resolution preset (longest side in pixels)
  - Options: `"1K"`, `"2K"`
  - Default: `"2K"`

- **`aspect_ratio`** (`string`, _optional_):
  Aspect ratio of the edited image
  - Options: `"1:1"`, `"2:3"`, `"3:2"`, `"3:4"`, `"4:3"`, `"4:5"`, `"5:4"`, `"9:16"`, `"16:9"`, `"21:9"`
  - Default: `"1:1"`

**Required Parameters Example**:

```json
{
  "prompt": "<Text prompt for image editing>",
  "images": []
}
```

**Full Example**:

```json
{
  "images": [],
  "prompt": "<Text prompt for image editing>",
  "resolution": "2K",
  "aspect_ratio": "1:1"
}
```

## Variant: Text to Image Generation

Endpoint: `POST /vendors/google/v1/nano-banana-pro/generation`

### Input Schema
The API accepts the following input parameters:

- **`prompt`** (`string`, _required_):
  Text prompt for image generation

- **`resolution`** (`string`, _optional_):
  Output resolution preset (longest side in pixels)
  - Options: `"1K"`, `"2K"`
  - Default: `"2K"`

- **`aspect_ratio`** (`string`, _optional_):
  Aspect ratio of the generated image
  - Options: `"1:1"`, `"2:3"`, `"3:2"`, `"3:4"`, `"4:3"`, `"4:5"`, `"5:4"`, `"9:16"`, `"16:9"`, `"21:9"`
  - Default: `"1:1"`

**Required Parameters Example**:

```json
{
  "prompt": "<Text prompt for image generation>"
}
```

**Full Example**:

```json
{
  "prompt": "<Text prompt for image generation>",
  "resolution": "2K",
  "aspect_ratio": "1:1"
}
```

## Variant: /vendors/google/v1/nano-banana-pro/edit/{task_id}

Endpoint: `POST /vendors/google/v1/nano-banana-pro/edit/{task_id}`

## Variant: /vendors/google/v1/nano-banana-pro/generation/{task_id}

Endpoint: `POST /vendors/google/v1/nano-banana-pro/generation/{task_id}`

## Usage (Async Task API)
This model uses an async task-based workflow with two API calls:
1. **Submit a task** — `POST /v1/inference/nano-banana-pro` to create a generation task
2. **Poll for result** — `GET /v1/inference/nano-banana-pro/{task_id}` to check status and retrieve the result

### Step 1: Submit a Task

#### cURL
```bash
curl -X POST https://api.mulerouter.ai/vendors/google/v1/nano-banana-pro/edit \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer <YOUR_MULEROUTER_API_KEY>" \
  -d '{
    "prompt": "Your prompt here"
  }'
```

#### Python
```python
import requests

API_KEY = "<YOUR_MULEROUTER_API_KEY>"
ENDPOINT = "https://api.mulerouter.ai/vendors/google/v1/nano-banana-pro/edit"

response = requests.post(
    ENDPOINT,
    headers={
        "Content-Type": "application/json",
        "Authorization": f"Bearer {API_KEY}"
    },
    json={
        "prompt": "Your prompt here"
    }
)

result = response.json()
task_id = result["task_info"]["id"]
print(f"Task created: {task_id}")
```

#### Node.js / TypeScript
```typescript
const API_KEY = "<YOUR_MULEROUTER_API_KEY>";
const ENDPOINT = "https://api.mulerouter.ai/vendors/google/v1/nano-banana-pro/edit";

const response = await fetch(ENDPOINT, {
    method: "POST",
    headers: {
        "Content-Type": "application/json",
        "Authorization": `Bearer ${API_KEY}`
    },
    body: JSON.stringify({
        prompt: "Your prompt here"
    })
});

const result = await response.json();
const taskId = result.task_info.id;
console.log("Task created:", taskId);
```

#### Submit Response (202)
```json
{
  "task_info": {
    "id": "8e1e315e-b50d-4334-a231-be7d19a372f4",
    "status": "processing",
    "created_at": "2026-01-01T00:00:00.000Z"
  }
}
```

### Step 2: Poll for Result

Use the task ID from Step 1 to poll the status endpoint until the task is completed.

Endpoint: `GET /v1/inference/nano-banana-pro/{task_id}`

#### cURL
```bash
curl -X GET https://api.mulerouter.ai/vendors/google/v1/nano-banana-pro/edit/<TASK_ID> \
  -H "Authorization: Bearer <YOUR_MULEROUTER_API_KEY>"
```

#### Python
```python
import time

status_url = f"https://api.mulerouter.ai/vendors/google/v1/nano-banana-pro/edit/{task_id}"

while True:
    status = requests.get(status_url, headers={
        "Authorization": f"Bearer {API_KEY}"
    }).json()
    task_status = status["task_info"]["status"]
    if task_status in ("completed", "succeeded"):
        print("Result:", status)
        break
    elif task_status == "failed":
        print("Task failed:", status)
        break
    time.sleep(5)
```

#### Node.js / TypeScript
```typescript
const statusUrl = `https://api.mulerouter.ai/vendors/google/v1/nano-banana-pro/edit/${taskId}`;

while (true) {
    const statusRes = await fetch(statusUrl, {
        headers: { "Authorization": `Bearer ${API_KEY}` }
    });
    const status = await statusRes.json();
    const taskStatus = status.task_info.status;
    if (taskStatus === "completed" || taskStatus === "succeeded") {
        console.log("Result:", status);
        break;
    } else if (taskStatus === "failed") {
        console.log("Task failed:", status);
        break;
    }
    await new Promise(r => setTimeout(r, 5000));
}
```

## Additional Resources

### Documentation

- [Model Playground](https://www.mulerouter.ai/models/nano-banana-pro)
- [API Documentation](https://mulerouter.ai/docs/api-reference/endpoint/google/nano-banana-pro)

### MuleRouter Platform

- [Platform Documentation](https://www.mulerouter.ai/docs)
- [API Keys Management](https://www.mulerouter.ai/app/api-keys)