Name: levelsio Neon Tokyo Night Photo Generator API
Brand: magicapi
SKU: api-magicapi-rainy-tokyo-neon-images-generation-levelsio-neon-tokyo
Availability: InStock
Author: API Market

Shashank Agarwal

# 🌃 Neon Tokyo Night Photo Generator API (levelsio Neon Tokyo)

## Overview
Neon Tokyo is a FLUX LoRA by levelsio that generates photos in the style of rainy Tokyo nights with neon lights, as its developer describes it. The style is triggered by the word `TKYO` in the prompt, for example "a street at night rainy in style of TKYO".

Send a text `prompt` to generate up to 4 images. You can also pass an input `image` for image-to-image generation, or an `image` plus a `mask` for inpainting. Each request returns a list of image URLs.

## Features
- **Neon Tokyo style**: include the trigger word `TKYO` in the prompt to apply the trained style.
- **Two base models**: `dev` (default, best around 28 steps) or `schnell` (needs about 4 steps).
- **Image-to-image and inpainting**: pass `image` (and optionally `mask`); `prompt_strength` controls how much of the input image is replaced.
- **Aspect ratios**: `1:1` (default), `16:9`, `21:9`, `3:2`, `2:3`, `4:5`, `5:4`, `3:4`, `4:3`, `9:16`, `9:21`, or `custom` with `width` and `height` (256 to 1440).
- **Multiple outputs**: `num_outputs` from 1 to 4.
- **LoRA strength**: `lora_scale` for the neon Tokyo LoRA, plus an optional `extra_lora` with its own `extra_lora_scale`.
- **Output formats**: `webp` (default), `jpg` or `png`, with `output_quality` from 0 to 100.
- **Seed**: set `seed` to reproduce a result.

## Use Cases
1. **Night city scenes**: rainy streets, alleys and storefronts lit by neon signs.
2. **Cover and poster art**: moody neon visuals for music, books, events and thumbnails.
3. **Game and film concepts**: night city mood references for scenes and environments.
4. **Restyling images**: give an existing picture a rainy neon night look with image-to-image mode.

## Visual Examples
### Example Input
Prompt: `a street at night rainy in style of TKYO`

Settings: `model` `dev`, `num_outputs` 4, `aspect_ratio` `4:3`, `num_inference_steps` 28, `guidance_scale` 3.5, `lora_scale` 0.75, `extra_lora_scale` 0.8, `output_format` `jpg`, `output_quality` 80.

### Example Output
The request returned 4 images:

![Neon Tokyo output 1: a rainy street at night lit by neon signs](https://blog.api.market/wp-content/uploads/2026/10/neon-tokyo-output-1-mcwetrrf-api-market.jpg)
![Neon Tokyo output 2: a rainy street at night lit by neon signs](https://blog.api.market/wp-content/uploads/2026/10/neon-tokyo-output-2-mcwetrrf-api-market.jpg)
![Neon Tokyo output 3: a rainy street at night lit by neon signs](https://blog.api.market/wp-content/uploads/2026/10/neon-tokyo-output-3-mcwetrrf-api-market.jpg)
![Neon Tokyo output 4: a rainy street at night lit by neon signs](https://blog.api.market/wp-content/uploads/2026/10/neon-tokyo-output-4-mcwetrrf-api-market.jpg)

## Input Parameters
| Field | Type | Required | Default | Allowed values | Description |
| --- | --- | --- | --- | --- | --- |
| `prompt` | string | Yes |  |  | Prompt for the generated image. Include the trigger word `TKYO` to activate the trained neon Tokyo style. |
| `image` | string (URL) | No | none |  | Input image (jpeg, png, gif or webp) for image-to-image or inpainting mode. If provided, `aspect_ratio`, `width` and `height` are ignored. |
| `mask` | string (URL) | No | none |  | Image mask for inpainting mode. If provided, `aspect_ratio`, `width` and `height` are ignored. |
| `aspect_ratio` | string | No | `1:1` | `1:1`, `16:9`, `21:9`, `3:2`, `2:3`, `4:5`, `5:4`, `3:4`, `4:3`, `9:16`, `9:21`, `custom` | Aspect ratio of the generated image. `custom` uses `width` and `height` and runs in bf16 mode. |
| `height` | integer | No | none | 256 to 1440 | Height of the generated image. Only used when `aspect_ratio` is `custom`. Rounded to the nearest multiple of 16. Not compatible with `go_fast`. |
| `width` | integer | No | none | 256 to 1440 | Width of the generated image. Only used when `aspect_ratio` is `custom`. Rounded to the nearest multiple of 16. Not compatible with `go_fast`. |
| `prompt_strength` | number | No | `0.8` | 0 to 1 | Prompt strength in image-to-image mode. 1.0 fully replaces the information in the input image. |
| `model` | string | No | `dev` | `dev`, `schnell` | Base model. `dev` works best with about 28 steps, `schnell` needs only 4. |
| `num_outputs` | integer | No | `1` | 1 to 4 | Number of images to generate. |
| `num_inference_steps` | integer | No | `28` | 1 to 50 | Number of denoising steps. More steps can give more detail but take longer. |
| `guidance_scale` | number | No | `3` | 0 to 10 | Guidance scale. Lower values can give more realistic images. Good values to try are 2, 2.5, 3 and 3.5. |
| `seed` | integer | No | none |  | Random seed for reproducible generation. |
| `output_format` | string | No | `webp` | `webp`, `jpg`, `png` | Format of the output images. |
| `output_quality` | integer | No | `80` | 0 to 100 | Quality of saved images. 100 is best. Not used for `png`. |
| `disable_safety_checker` | boolean | No | `false` |  | Disable the safety checker for generated images. |
| `go_fast` | boolean | No | `false` |  | Run a speed-optimized (fp8 quantized) model. Set to `false` to run in the original bf16. |
| `megapixels` | string | No | `1` | `1`, `0.25` | Approximate number of megapixels of the generated image. |
| `lora_scale` | number | No | `1` | -1 to 3 | How strongly the neon Tokyo LoRA is applied. Values between 0 and 1 work for standard inference; with `go_fast` a 1.5x multiplier is applied. |
| `extra_lora` | string | No | none |  | Extra LoRA weights to load: a Hugging Face model, a CivitAI model or a direct `.safetensors` file URL. |
| `extra_lora_scale` | number | No | `1` | -1 to 3 | How strongly the extra LoRA is applied. Values between 0 and 1 work for standard inference; with `go_fast` a 1.5x multiplier is applied. |

## Pricing
- **Pay As You Go (PAYG) pricing**: you only pay for what you use.
- **Price**: $0.033 per API unit.
- **1 API unit = 1 request with up to 17 seconds of processing.** A request that runs longer is billed one more unit for each started 17-second block (for example, more than 17 and up to 34 seconds of processing is 2 units, $0.066).
- Settings that add work, such as more `num_outputs` or more `num_inference_steps`, take longer and can use more units.
- **Example cost**: the example request (4 images at 28 steps) took 33.9 seconds of processing, which is 2 started 17-second blocks: 2 API units, $0.066. A request for one image (the default `num_outputs` 1) usually fits in one 17-second block: 1 API unit, $0.033.
- **Failed or cancelled predictions are not billed.**
- **GET requests to check prediction status** are free and don't count toward API unit usage.
- **No monthly subscription fee.**

## API Documentation

### Base URL
All API requests go to: `https://prod.api.market/api/v1/magicapi/rainy-tokyo-neon-images-generation-levelsio-neon-tokyo`

### Authentication
Send your api.market API key in the `x-api-market-key` header:
```
x-api-market-key: YOUR_API_MARKET_KEY
```

### Asynchronous Processing
**IMPORTANT**: This API is asynchronous. The flow is:
1. Send a POST request to `/predictions`. A successful request returns **201 Created** with a prediction `id` and a `status` of `starting` or `processing`.
2. Poll GET `/predictions/{id}` with that ID.
3. Keep polling while `status` is `starting` or `processing`.
4. Stop when `status` is `succeeded` (read `output`), `failed` or `canceled` (read `error`).

A polling interval of 1 second and an overall timeout of 600 seconds (10 minutes) work well.

### Endpoints

#### Create a Prediction
```bash
curl -X POST "https://prod.api.market/api/v1/magicapi/rainy-tokyo-neon-images-generation-levelsio-neon-tokyo/predictions" \
  -H "Content-Type: application/json" \
  -H "x-api-market-key: YOUR_API_MARKET_KEY" \
  -d '{
    "version": "64d1f3c37e1702a4b659e6373b3e4b7b4d1feda75337d9581bc3e893df516436",
    "input": {
      "model": "dev",
      "prompt": "a street at night rainy in style of TKYO",
      "lora_scale": 0.75,
      "num_outputs": 4,
      "aspect_ratio": "4:3",
      "output_format": "jpg",
      "guidance_scale": 3.5,
      "output_quality": 80,
      "extra_lora_scale": 0.8,
      "num_inference_steps": 28
    }
  }'
```

Response (201 Created):
```json
{
  "id": "mcwetrrfkhrmt0d1f1kvds1snr",
  "version": "64d1f3c37e1702a4b659e6373b3e4b7b4d1feda75337d9581bc3e893df516436",
  "status": "starting",
  "created_at": "2026-10-26T13:29:42.726Z"
}
```

#### Check Prediction Status
```bash
curl -X GET "https://prod.api.market/api/v1/magicapi/rainy-tokyo-neon-images-generation-levelsio-neon-tokyo/predictions/{id}" \
  -H "x-api-market-key: YOUR_API_MARKET_KEY"
```

Response when the prediction has finished (200 OK):
```json
{
  "id": "mcwetrrfkhrmt0d1f1kvds1snr",
  "status": "succeeded",
  "output": [
    "https://blog.api.market/wp-content/uploads/2026/10/neon-tokyo-output-1-mcwetrrf-api-market.jpg",
    "https://blog.api.market/wp-content/uploads/2026/10/neon-tokyo-output-2-mcwetrrf-api-market.jpg",
    "https://blog.api.market/wp-content/uploads/2026/10/neon-tokyo-output-3-mcwetrrf-api-market.jpg",
    "https://blog.api.market/wp-content/uploads/2026/10/neon-tokyo-output-4-mcwetrrf-api-market.jpg"
  ],
  "error": null,
  "created_at": "2026-10-26T13:29:42.726Z",
  "started_at": "2026-10-26T13:29:42.754Z",
  "completed_at": "2026-10-26T13:30:16.648Z"
}
```

`output` is a list of image URLs, one per requested output (`num_outputs`).

### Status Codes
- **201 Created**: prediction created (POST `/predictions`). This is the normal success response.
- **200 OK**: prediction details returned (GET `/predictions/{id}`).
- **400 Bad Request**: invalid input, for example a missing `prompt` or an `aspect_ratio` that is not in the list.
- **401 Unauthorized**: missing or invalid API key.
- **404 Not Found**: no prediction with that ID.
- **429 Too Many Requests**: rate limit exceeded.
- **500 Internal Server Error** or **503 Service Unavailable**: temporary server problem; retry later.

## Rate Limits
Requests are rate limited to keep usage fair. If you receive a 429 response, wait and retry with exponential backoff.

## Error Handling
- Check the HTTP status code of every request and read the response body for the error message.
- A prediction can also end with `status` set to `failed`; the `error` field then explains why. Failed predictions are not billed.
- Retry 429, 500 and 503 responses with exponential backoff. Fix the input before retrying a 400.

## Implementation Guide
### Example Code

#### JavaScript
```javascript
const apiKey = "YOUR_API_MARKET_KEY";
const baseUrl = "https://prod.api.market/api/v1/magicapi/rainy-tokyo-neon-images-generation-levelsio-neon-tokyo";

async function createAndWaitForPrediction(input) {
  const response = await fetch(`${baseUrl}/predictions`, {
    method: "POST",
    headers: {
      "Content-Type": "application/json",
      "x-api-market-key": apiKey
    },
    body: JSON.stringify({ version: "64d1f3c37e1702a4b659e6373b3e4b7b4d1feda75337d9581bc3e893df516436", input })
  });

  if (response.status !== 201) {
    throw new Error(`API error: ${response.status} ${await response.text()}`);
  }

  const prediction = await response.json();
  return pollForResults(prediction.id);
}

const delay = (ms) => new Promise((resolve) => setTimeout(resolve, ms));

async function pollForResults(predictionId) {
  const maxTimeout = 600000;
  const pollInterval = 1000;
  const startTime = Date.now();

  while (Date.now() - startTime < maxTimeout) {
    const response = await fetch(`${baseUrl}/predictions/${predictionId}`, {
      headers: { "x-api-market-key": apiKey }
    });

    if (!response.ok) {
      throw new Error(`API error: ${response.status} ${await response.text()}`);
    }

    const prediction = await response.json();

    if (prediction.status === "succeeded") {
      return prediction.output;
    }

    if (prediction.status === "failed" || prediction.status === "canceled") {
      throw new Error(`Prediction ${prediction.status}: ${prediction.error || "Unknown error"}`);
    }

    await delay(pollInterval);
  }

  throw new Error("Prediction timed out after 10 minutes");
}

createAndWaitForPrediction({
  "model": "dev",
  "prompt": "a street at night rainy in style of TKYO",
  "lora_scale": 0.75,
  "num_outputs": 4,
  "aspect_ratio": "4:3",
  "output_format": "jpg",
  "guidance_scale": 3.5,
  "output_quality": 80,
  "extra_lora_scale": 0.8,
  "num_inference_steps": 28
}).then((output) => console.log(output));
```

#### Python
```python
import time
import requests

API_KEY = "YOUR_API_MARKET_KEY"
BASE_URL = "https://prod.api.market/api/v1/magicapi/rainy-tokyo-neon-images-generation-levelsio-neon-tokyo"


def create_and_wait_for_prediction(input_data):
    headers = {"Content-Type": "application/json", "x-api-market-key": API_KEY}
    response = requests.post(
        f"{BASE_URL}/predictions",
        headers=headers,
        json={"version": "64d1f3c37e1702a4b659e6373b3e4b7b4d1feda75337d9581bc3e893df516436", "input": input_data},
    )

    if response.status_code != 201:
        raise Exception(f"API error: {response.status_code} {response.text}")

    return poll_for_results(response.json()["id"])


def poll_for_results(prediction_id):
    max_timeout = 600
    poll_interval = 1
    headers = {"x-api-market-key": API_KEY}
    start_time = time.time()

    while time.time() - start_time < max_timeout:
        response = requests.get(f"{BASE_URL}/predictions/{prediction_id}", headers=headers)

        if response.status_code != 200:
            raise Exception(f"API error: {response.status_code} {response.text}")

        prediction = response.json()

        if prediction["status"] == "succeeded":
            return prediction["output"]

        if prediction["status"] in ("failed", "canceled"):
            raise Exception(f"Prediction {prediction['status']}: {prediction.get('error') or 'Unknown error'}")

        time.sleep(poll_interval)

    raise Exception("Prediction timed out after 10 minutes")


print(create_and_wait_for_prediction({
    "model": "dev",
    "prompt": "a street at night rainy in style of TKYO",
    "lora_scale": 0.75,
    "num_outputs": 4,
    "aspect_ratio": "4:3",
    "output_format": "jpg",
    "guidance_scale": 3.5,
    "output_quality": 80,
    "extra_lora_scale": 0.8,
    "num_inference_steps": 28
}))
```

## FAQ
- **How do I get the neon Tokyo look?**
  - Add the trigger word to your prompt, for example "a street at night rainy in style of TKYO". Raise or lower `lora_scale` to make the style stronger or weaker.
- **Should I use `dev` or `schnell`?**
  - `dev` (default) works best with about 28 `num_inference_steps`. `schnell` needs only about 4 steps, so it finishes faster and uses fewer API units.
- **Can I restyle an existing image?**
  - Yes. Pass it as `image` and set `prompt_strength` (default 0.8; 1.0 replaces the input entirely). Add a `mask` to repaint only part of the image.
- **How many images does one request return?**
  - Between 1 and 4, set by `num_outputs`. More images take longer to generate, so a request can use more than one 17-second API unit ($0.033 each).
- **Is the 201 response from POST an error?**
  - No. 201 Created means the prediction was accepted. Use the returned `id` to poll GET `/predictions/{id}` until `status` is `succeeded`, `failed` or `canceled`.
- **What if my prediction fails?**
  - Failed predictions are not billed. Read the `error` field, adjust the input and try again.

## Contact Us
If you have any questions or need assistance, you can reach out to us through the embedded chat on the bottom right corner of the page or send an email to hello@magicapi.com.


Generate rainy neon night photos in a Tokyo street style with levelsio's Neon Tokyo FLUX LoRA, from text or an image. $0.033 per request.