# Image2Pixel API

> Converts images to clean pixel art with palette limits, scale detection, and optional transparent background

Turns an input image into pixel art and returns the processed image plus a scaled version as PNG files. You can cap the palette with `max_colors` (1 to 256), supply a `fixed_palette` of hex colors, or force a `scale` from 1 to 64. Several downscale methods, cleanup options and background removal modes are available. The response also reports the number of colors, the palette and the detected pixel scale.

- Model id: `image2pixel`
- Made by: Utility
- Category: Image Editing
- Output: image
- Charged by usage (up to $0.153 per run, typical $0.002; the rest of the hold is refunded): $0.00017 / second of processing time (cheapest run $0.002 (2 API credits))
- Run: `POST https://api.doitong.ru/v1/run/image2pixel`
- Quote: `POST https://api.doitong.ru/v1/quote/image2pixel`
- Docs page: https://doitong.com/en-GB/developers/models/image2pixel

## Pricing

Price: **$0.00017 / second of processing time**. Charged by actual usage at $0.00017 / second of processing time: up to $0.153 per run (the quote gives the maximum for your input), the rest is refunded automatically after the run. A typical run costs $0.002.

The full example input below holds up to $0.153 = 153 API credits (up to 900 s of processing time × $0.00017) when it starts; a typical run costs $0.002 (2 API credits).

Prices are the upstream price in USD, paid from the API balance in API credits (1000 API credits = 1 USD). This model is charged by usage: `POST https://api.doitong.ru/v1/quote/image2pixel` with the same JSON body returns the most the run can cost for that input (`credits`, debited when the run starts); after the run you pay the usage the model reports and the rest is refunded automatically. An input whose maximum would exceed the per-run limit is refused with 422. Invalid input is never charged and failed or canceled requests are refunded automatically.

## Input parameters

Send either the canonical name or the native field name (not both). Canonical names are the same across models.

| Parameter | Canonical name | Type | Required | Default | Allowed values / range | Description |
|---|---|---|---|---|---|---|
| `image_url` | `image` | string (image URL) | yes |  |  | Source image URL for conversion into improved pixel art. |
| `max_colors` |  | integer \| null | no | `32` | 1 – 256 | Output palette color cap; None allows an unlimited number of colors. |
| `auto_color_detect` |  | boolean | no | `false` |  | Enables automatic selection of the optimal color count. |
| `fixed_palette` |  | array of string \| null | no |  |  | Optional predefined palette specified with hex strings, such as ['#000000', '#ffffff']. |
| `detect_method` |  | enum (string) | no | `auto` | `auto`, `runs`, `edge` | Method used to detect the scale. |
| `scale` |  | integer \| null | no |  | 1 – 64 | Pixel scale to enforce; None enables automatic detection. |
| `downscale_method` |  | enum (string) | no | `dominant` | `dominant`, `median`, `mode`, `mean`, `content-adaptive` | Method used to downscale into pixel art. |
| `trim_borders` |  | boolean | no | `false` |  | Controls trimming of image borders. |
| `transparent_background` |  | boolean | no | `false` |  | Background removal through transparency: once image correction finishes, connected regions of color that touch an image edge become transparent. |
| `cleanup_morph` |  | boolean | no | `false` |  | Noise removal using morphological operations. |
| `cleanup_jaggy` |  | boolean | no | `false` |  | Jagged-edge cleanup by removing isolated diagonal pixels. |
| `snap_grid` |  | boolean | no | `true` |  | Snaps the output onto the pixel grid. |
| `alpha_threshold` |  | integer | no | `128` | 0 – 255 | Threshold for binarizing alpha, from 0 to 255. |
| `dominant_color_threshold` |  | number | no | `0.05` | 0 – 1 | Threshold for dominant colors, from 0.0 to 1.0. |
| `background_tolerance` |  | integer | no | `0` | 0 – 255 | Tolerance for the background, from 0 to 255. |
| `background_mode` |  | enum (string) | no | `corners` | `edges`, `corners`, `midpoints` | Starting locations for flood-fill during background removal. |

File parameters (`image`) take a public `https://` URL, a `data:` URI up to 10 MB, or the `file_url` of an upload from `POST /files`.

## Example input

Required parameters only:

```json
{
  "image": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
}
```

A fuller example:

```json
{
  "image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
}
```

## Run it

Set `DOITONG_API_KEY` to a key from https://doitong.com/developers/dashboard#keys. `?wait=20` returns the finished request in the same call when it is ready within 20 seconds; otherwise poll `GET /requests/{id}` (it also accepts `?wait=`), or pass `webhook_url`.

### curl

```bash
# Run the model; wait up to 20 s for the finished result in the same call
curl -X POST "https://api.doitong.ru/v1/run/image2pixel?wait=20" \
  -H "Authorization: Bearer $DOITONG_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
  }'

# Still "queued" or "running"? Poll with the id from the response:
curl "https://api.doitong.ru/v1/requests/REQUEST_ID?wait=20" \
  -H "Authorization: Bearer $DOITONG_API_KEY"
```

### JavaScript

```javascript
const API = "https://api.doitong.ru/v1";
const headers = {
  "Authorization": "Bearer " + process.env.DOITONG_API_KEY,
  "Content-Type": "application/json",
};

// Run the model; wait up to 20 s for the finished result in the same call
let res = await fetch(API + "/run/image2pixel?wait=20", {
  method: "POST",
  headers,
  body: JSON.stringify({
    "image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
  }),
});
let request = await res.json();
if (!res.ok) throw new Error(request.error.code + ": " + request.error.message);

// Not finished yet: long-poll until it is
while (request.status === "queued" || request.status === "running") {
  res = await fetch(API + "/requests/" + request.id + "?wait=20", { headers });
  request = await res.json();
}
if (request.status !== "succeeded") throw new Error(JSON.stringify(request.error));
console.log(request.outputs.map((o) => o.url), request.cost.credits + " API credits ($" + request.cost.usd + ")");
```

### Python

```python
import os
import requests

API = "https://api.doitong.ru/v1"
headers = {"Authorization": "Bearer " + os.environ["DOITONG_API_KEY"]}

payload = {
    "image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
}

# Run the model; wait up to 20 s for the finished result in the same call
r = requests.post(API + "/run/image2pixel", params={"wait": 20}, json=payload, headers=headers)
request = r.json()
if not r.ok:
    raise RuntimeError(request["error"]["code"] + ": " + request["error"]["message"])

# Not finished yet: long-poll until it is
while request["status"] in ("queued", "running"):
    request = requests.get(API + "/requests/" + request["id"], params={"wait": 20}, headers=headers).json()
if request["status"] != "succeeded":
    raise RuntimeError(request["error"])
print([o["url"] for o in request["outputs"]], request["cost"]["credits"], "API credits", "$" + request["cost"]["usd"])
```

### MCP

The MCP server at `https://api.doitong.ru/v1/mcp` exposes the tools `search_models`, `get_model`, `quote`, `run`, `get_request`, `cancel_request`, `list_requests`, `create_upload` and `get_account`. A `run` call for this model:

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "run",
    "arguments": {
      "model_id": "image2pixel",
      "input": {
        "image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
      },
      "wait_seconds": 20
    }
  }
}
```

## Response

Every call returns a request object. `status` is one of `queued`, `running`, `succeeded`, `failed`, `canceled`. `outputs` lists the generated files with URLs on our storage (kept forever, or 7 days with `?storage=temporary`; `storage` says which); `output` is the model's own result object with the same URLs.

```json
{
  "id": "req_8k2m4q7x1z9c3v5b6n0p2r4t",
  "object": "request",
  "model": "image2pixel",
  "status": "succeeded",
  "created_at": "2026-10-20T12:00:00Z",
  "started_at": "2026-10-20T12:00:02Z",
  "completed_at": "2026-10-20T12:00:09Z",
  "queue_position": null,
  "cost": {
    "credits": 153,
    "usd": "0.153",
    "charged": 153,
    "refunded": 0
  },
  "input": {
    "image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
  },
  "output": {
    "pixel_scale": 21,
    "palette": [
      "string"
    ],
    "num_colors": 10,
    "images": [
      {
        "url": "https://content.doitong.com/dev-api/2026/10/20/req_8k2m4q7x1z9c3v5b6n0p2r4t/0-output.jpeg",
        "content_type": "image/jpeg",
        "file_name": "0-output.jpeg",
        "file_size": 1843200,
        "width": 1024,
        "height": 768
      }
    ]
  },
  "outputs": [
    {
      "type": "image",
      "url": "https://content.doitong.com/dev-api/2026/10/20/req_8k2m4q7x1z9c3v5b6n0p2r4t/0-output.jpeg",
      "content_type": "image/jpeg",
      "file_size": 1843200,
      "width": 1024,
      "height": 768,
      "stored": true
    }
  ],
  "error": null,
  "metrics": {
    "inference_seconds": 6.8
  },
  "urls": {
    "self": "https://api.doitong.ru/v1/requests/req_8k2m4q7x1z9c3v5b6n0p2r4t",
    "cancel": "https://api.doitong.ru/v1/requests/req_8k2m4q7x1z9c3v5b6n0p2r4t/cancel"
  }
}
```

### Output fields (`output`)

| Field | Type | Description |
|---|---|---|
| `pixel_scale` | integer | Pixel scale detected in the input. |
| `palette` | array of string | Color palette from the processed media. |
| `num_colors` | integer | Color count for the processed media. |
| `images` | array of object {url, content_type, file_name, file_size, width, height} | Processed pixel-art and scaled images, both in PNG format. |

## Related models

- [FLUX.1 Kontext \[pro\]](https://doitong.com/developers/models/flux-pro/kontext.md) (`flux-pro/kontext`): $0.04 / image
- [GPT Image 2.5 Edit](https://doitong.com/developers/models/openai/gpt-image-2.5/flare/edit.md) (`openai/gpt-image-2.5/flare/edit`): from $0.12 / image
- [Nano Banana 2 Edit](https://doitong.com/developers/models/nano-banana-2/edit.md) (`nano-banana-2/edit`): from $0.06 / image
- [Nano Banana Pro Edit](https://doitong.com/developers/models/nano-banana-pro/edit.md) (`nano-banana-pro/edit`): from $0.15 / image
- [Qwen Image 3 Edit](https://doitong.com/developers/models/alibaba/qwen-image-3/edit.md) (`alibaba/qwen-image-3/edit`): from $0.04 / image
- [Seedream 5.0 Pro Edit](https://doitong.com/developers/models/bytedance/seedream/v5/pro/edit.md) (`bytedance/seedream/v5/pro/edit`): from $0.0675 / run of output image
- [Age Modify](https://doitong.com/developers/models/image-apps-v2/age-modify.md) (`image-apps-v2/age-modify`): $0.04 / image
- [Cartoonify](https://doitong.com/developers/models/cartoonify.md) (`cartoonify`): $0.10 / image

## Links

- HTML page: https://doitong.com/developers/models/image2pixel
- All models and guides for AI agents: https://doitong.com/developers/llms.txt
- OpenAPI 3.1 with this model's typed request body: https://doitong.com/developers/openapi.json?model=image2pixel
- Errors: https://doitong.com/developers/docs/errors
- Model details as JSON: https://api.doitong.ru/v1/models/image2pixel