# google/nano-banana/text-to-image

> Hyper-realistic and physics-aware visuals can be produced via natural language instructions using Nano Banana, an advanced image generation model that also facilitates flexible style transformations.

## Overview

- **Endpoint**: `https://api.shortapi.ai/api/v1/job/create`
- **Model ID**: `google/nano-banana/text-to-image`
- **Category**: text-to-image
- **Kind**: inference

## Pricing

Your request will cost $0.03 per image.

For more details, please check our pricing page.

## API Information

This model can be used via our HTTP API or more conveniently via our client libraries.
See the input and output schema below, as well as the usage examples.

### Input Schema

The API accepts the following input parameters:

- **`prompt`** (`string`, _required_):
  Text prompt
  - Examples: "An action shot of a black lab swimming..."

- **`num_images`** (`int`, _optional_):
  The number of images to generate
  - Default: `1`
  - Options: "1", "2", "3", "4"

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

### Output Schema

The API returns a JSON response with a `job_id` for tracking the request status.

**Create Job Response:**
```json
{
  "code": 0,
  "data": {
    "amount": "0.02",
    "job_id": "<JOB_ID>"
  }
}
```

**Query Job Result (when status is 2, meaning succeeded):**
```json
{
  "code": 0,
  "data": {
    "status": 2,
    "result": {
      "images": [{ "url": "https://..." }]
    }
  }
}
```

## Use Example

To use this model, make an HTTP POST request to the API endpoint, then poll for results using the returned `job_id`.

### Bash (cURL)
```bash
# Step 1: Create a job
response=$(curl --request POST \
  --url https://api.shortapi.ai/api/v1/job/create \
  --header "Authorization: Bearer $SHORTAPI_KEY" \
  --header "Content-Type: application/json" \
  --data '{
  "model": "google/nano-banana/text-to-image",
  "args": {
    "prompt": "A [coffee machine] rendered as if entirely hand-knitted from chunky wool yarn, impossibly functional-looking despite being soft craft. Visible stitches, cable knit patterns on grip areas, ribbing where flexibility is needed, yarn ends neatly tucked. Positioned against a cozy lifestyle backdrop or clean wool-texture studio. Scandinavian hygge meets industrial design. Warm soft lighting emphasizing the yarn texture, the handmade quality, the absurd coziness of machinery. Sharp focus on stitch detail, warm saturated fiber colors, lifestyle product aesthetic, ultra-high resolution, the hard made soft.",
    "aspect_ratio": "16:9"
  },
  "callback_url": "CALLBACK_URL"
}')
JOB_ID=$(echo "$response" | grep -o '"job_id": *"[^"]*"' | sed 's/"job_id": *//; s/"//g')

# Step 2: Poll for results
curl --request GET \
  --url "https://api.shortapi.ai/api/v1/job/query?id=$JOB_ID" \
  --header "Authorization: Bearer $SHORTAPI_KEY"
```

### JavaScript (Fetch API)
```javascript
// Step 1: Create a job
const response = await fetch(`https://api.shortapi.ai/api/v1/job/create`, {
  method: "POST",
  headers: {
    "Authorization": `Bearer ${SHORTAPI_KEY}`,
    "Content-Type": "application/json"
  },
  body: JSON.stringify({
    "model": "google/nano-banana/text-to-image",
    "args": {
      "prompt": "A [coffee machine] rendered as if entirely hand-knitted from chunky wool yarn, impossibly functional-looking despite being soft craft. Visible stitches, cable knit patterns on grip areas, ribbing where flexibility is needed, yarn ends neatly tucked. Positioned against a cozy lifestyle backdrop or clean wool-texture studio. Scandinavian hygge meets industrial design. Warm soft lighting emphasizing the yarn texture, the handmade quality, the absurd coziness of machinery. Sharp focus on stitch detail, warm saturated fiber colors, lifestyle product aesthetic, ultra-high resolution, the hard made soft.",
      "aspect_ratio": "16:9"
    },
    "callback_url": "CALLBACK_URL"
})
});
const data = await response.json();
const JOB_ID = data.job_id;

// Step 2: Poll for results
const result = await fetch(`https://api.shortapi.ai/api/v1/job/query?id=${JOB_ID}`, {
  method: "GET",
  headers: {
    "Authorization": `Bearer ${SHORTAPI_KEY}`
  }
});
const resultData = await result.json();
console.log(resultData);
```

### Python (Requests)
```python
import requests

# Step 1: Create a job
url = "https://api.shortapi.ai/api/v1/job/create"
payload = {
  "model": "google/nano-banana/text-to-image",
  "args": {
    "prompt": "A [coffee machine] rendered as if entirely hand-knitted from chunky wool yarn, impossibly functional-looking despite being soft craft. Visible stitches, cable knit patterns on grip areas, ribbing where flexibility is needed, yarn ends neatly tucked. Positioned against a cozy lifestyle backdrop or clean wool-texture studio. Scandinavian hygge meets industrial design. Warm soft lighting emphasizing the yarn texture, the handmade quality, the absurd coziness of machinery. Sharp focus on stitch detail, warm saturated fiber colors, lifestyle product aesthetic, ultra-high resolution, the hard made soft.",
    "aspect_ratio": "16:9"
  },
  "callback_url": "CALLBACK_URL"
}
headers = {
    "Authorization": f"Bearer {SHORTAPI_KEY}",
    "Content-Type": "application/json"
}

response = requests.post(url, headers=headers, json=payload)
data = response.json()
JOB_ID = data.get("job_id")

# Step 2: Poll for results
result_url = f"https://api.shortapi.ai/api/v1/job/query?id={JOB_ID}"
result = requests.get(result_url, headers={"Authorization": f"Bearer {SHORTAPI_KEY}"})
print(result.json())
```