# Nano Banana 2 Lite Text to Image

> Generate 1K (roughly 1 megapixel) images from a text prompt at the lowest price in the Nano Banana family, with 14 aspect ratios from square to ultra-wide banner.

## Overview

- **Endpoint**: `https://queue.modelrunner.run/google/nano-banana-2-lite`
- **Model ID**: `google/nano-banana-2-lite`
- **Category**: text-to-image
- **Kind**: inference
- **Tags**: text-to-image, image-generation, nano-banana, gemini, fast, low-cost, budget, banner, 1k

## Pricing

- **Price**: $0.034 per output

## Request Lifecycle

This model runs on the ModelRunner **asynchronous queue API** — a single POST does not return the output. Every call requires an `Authorization: Key $MODEL_RUNNER_KEY` header. Run three steps:

1. **Submit** — `POST https://queue.modelrunner.run/google/nano-banana-2-lite` with a JSON body holding the input fields at the top level. The body may also include a reserved top-level `metadata` object — a flat string map (max 16 keys, key ≤64 / value ≤512 chars) stored on the request for your own tagging. It is never sent to the model; filter your request history with `GET https://queue.modelrunner.run/requests?metadata=<url-encoded JSON>` (exact key=value matches, AND-ed). The response carries request handles only (no output yet):

```json
{
  "status": "IN_QUEUE",
  "request_id": "<21-char id>",
  "status_url": "https://queue.modelrunner.run/google/nano-banana-2-lite/requests/<request_id>/status",
  "response_url": "https://queue.modelrunner.run/google/nano-banana-2-lite/requests/<request_id>",
  "cancel_url": "https://queue.modelrunner.run/google/nano-banana-2-lite/requests/<request_id>/cancel"
}
```

2. **Poll status** — `GET <status_url>` until `status` is `COMPLETED`. Possible values are `IN_QUEUE`, `IN_PROGRESS`, `COMPLETED`, `FAILED`, `CANCELLED`. A `FAILED` request responds with HTTP 400 and an `error` field.

3. **Read result** — `GET <response_url>`. Returns the finished request, including the generated `output`:

```json
{ "id": "<request_id>", "status": "COMPLETED", "output": ..., "input": ... }
```

The JavaScript and Python SDKs below perform steps 2–3 for you. In any language without an SDK (Swift, Go, Kotlin, etc.) you must implement the polling loop and the final result fetch yourself — see the cURL example for the full flow.

### Input Schema

- **`prompt`** (`string`, _required_):
  The text prompt describing the image to generate. Describe subject, framing, lighting and style; quote any text you want rendered inside the image.

- **`aspect_ratio`** (`AspectRatio`, _optional_):
  Shape of the generated image. Output is always 1K (roughly 1 megapixel, 1024x1024 for 1:1), so this changes the proportions only, never the resolution.
  - Default: `"1:1"`
  - Options: `"1:1"`, `"16:9"`, `"9:16"`, `"4:3"`, `"3:4"`, `"3:2"`, `"2:3"`, `"4:5"`, `"5:4"`, `"21:9"`, `"1:4"`, `"4:1"`, `"1:8"`, `"8:1"`

- **`safety_settings`** (`array<object>`, _optional_):
  Per-category content-blocking thresholds. Omit to use the service defaults. This setting can only be configured via the API.

### Output Schema

_No `Output` schema properties are available._

## Default Example

**Input**

```json
{
  "prompt": "An ultra-wide banner of a bullet train slicing through terraced rice paddies at golden hour, motion blur on the foreground grass, editorial travel photography",
  "aspect_ratio": "21:9"
}
```

**Output**

```json
"https://media.modelrunner.ai/mAgWMeYXbJ7u4LGfxbdE7.jpeg"
```
## Usage Examples

### cURL

The queue API is asynchronous: submit the request, poll `status_url` until it is `COMPLETED`, then read the result from `response_url`. Requires `jq`.

```bash
# 1. Submit the request (returns request handles, not the output)
SUBMIT=$(curl --silent --request POST \
  --url https://queue.modelrunner.run/google/nano-banana-2-lite \
  --header "Authorization: Key $MODEL_RUNNER_KEY" \
  --header "Content-Type: application/json" \
  --data '{
  "prompt": "An ultra-wide banner of a bullet train slicing through terraced rice paddies at golden hour, motion blur on the foreground grass, editorial travel photography",
  "aspect_ratio": "21:9"
}')

STATUS_URL=$(echo "$SUBMIT" | jq -r '.status_url')
RESPONSE_URL=$(echo "$SUBMIT" | jq -r '.response_url')

# 2. Poll until the request leaves the queue / in-progress state
while true; do
  STATUS=$(curl --silent --url "$STATUS_URL" \
    --header "Authorization: Key $MODEL_RUNNER_KEY" | jq -r '.status')
  echo "Status: $STATUS"
  case "$STATUS" in
    COMPLETED) break ;;
    FAILED|CANCELLED) echo "Request $STATUS"; exit 1 ;;
  esac
  sleep 1
done

# 3. Read the finished request, including the generated output
curl --silent --url "$RESPONSE_URL" \
  --header "Authorization: Key $MODEL_RUNNER_KEY"
```

### JavaScript

```javascript
import { modelrunner } from "@modelrunner/client";

const result = await modelrunner.subscribe("google/nano-banana-2-lite", {
  input: {
  "prompt": "An ultra-wide banner of a bullet train slicing through terraced rice paddies at golden hour, motion blur on the foreground grass, editorial travel photography",
  "aspect_ratio": "21:9"
}
});

console.log(result.data);
```

### Python

```python
import asyncio
import modelrunner_ai

async def main():
    response = await modelrunner_ai.submit_async(
        "google/nano-banana-2-lite",
        arguments={
  "prompt": "An ultra-wide banner of a bullet train slicing through terraced rice paddies at golden hour, motion blur on the foreground grass, editorial travel photography",
  "aspect_ratio": "21:9"
}
    )
    result = await response.get()
    print(result["output"])

asyncio.run(main())
```

## Additional Resources

- [Playground](https://modelrunner.ai/models/google/nano-banana-2-lite)
- [OpenAPI Schema](https://modelrunner.ai/models/google/nano-banana-2-lite/openapi.json)
- [LLM Instructions](https://modelrunner.ai/models/google/nano-banana-2-lite/llms.txt)