# Image2Pixel API

> يحوّل الصور إلى فن بكسل واضح مع حدود للوحة الألوان واكتشاف المقياس وخلفية شفافة اختيارية

يحوّل الصورة المُدخلة إلى فن بكسل ويعيد الصورة المعالجة ونسخة مكبّرة كملفات PNG. يمكنك تحديد حد أقصى للوحة الألوان عبر `max_colors` (من 1 إلى 256)، أو توفير `fixed_palette` من ألوان hex، أو فرض `scale` من 1 إلى 64. تتوفر عدة طرق لتصغير الحجم وخيارات للتنظيف وإزالة الخلفية. تعرض الاستجابة أيضًا عدد الألوان واللوحة ومقياس البكسل المكتشف.

- 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.com/v1/run/image2pixel`
- Quote: `POST https://api.doitong.com/v1/quote/image2pixel`
- Docs page: https://doitong.com/ar/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.com/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 |  |  | عنوان URL للصورة المصدر لتحويلها إلى فن بكسل محسّن. |
| `max_colors` |  | integer \| null | no | `32` | 1 – 256 | الحد الأقصى لألوان لوحة المخرج؛ تسمح None بعدد غير محدود من الألوان. |
| `auto_color_detect` |  | boolean | no | `false` |  | يمكّن الاختيار التلقائي للعدد الأمثل من الألوان. |
| `fixed_palette` |  | array of string \| null | no |  |  | لوحة ألوان محددة مسبقًا اختياريًا باستخدام سلاسل hex، مثل ['#000000', '#ffffff']. |
| `detect_method` |  | enum (string) | no | `auto` | `auto`, `runs`, `edge` | الطريقة المستخدمة لاكتشاف المقياس. |
| `scale` |  | integer \| null | no |  | 1 – 64 | مقياس البكسل المفروض؛ يتيح None الاكتشاف التلقائي. |
| `downscale_method` |  | enum (string) | no | `dominant` | `dominant`, `median`, `mode`, `mean`, `content-adaptive` | الطريقة المستخدمة لتصغير الصورة إلى فن بكسل. |
| `trim_borders` |  | boolean | no | `false` |  | يتحكم في قص حدود الصورة. |
| `transparent_background` |  | boolean | no | `false` |  | إزالة الخلفية عبر الشفافية: بعد انتهاء تصحيح الصورة، تصبح مناطق الألوان المتصلة التي تلامس حافة الصورة شفافة. |
| `cleanup_morph` |  | boolean | no | `false` |  | إزالة التشويش باستخدام العمليات المورفولوجية. |
| `cleanup_jaggy` |  | boolean | no | `false` |  | تنظيف الحواف المتعرجة بإزالة وحدات البكسل القطرية المعزولة. |
| `snap_grid` |  | boolean | no | `true` |  | يحاذي المخرج مع شبكة البكسل. |
| `alpha_threshold` |  | integer | no | `128` | 0 – 255 | عتبة تحويل ألفا إلى ثنائي، من 0 إلى 255. |
| `dominant_color_threshold` |  | number | no | `0.05` | 0 – 1 | عتبة الألوان السائدة، من 0.0 إلى 1.0. |
| `background_tolerance` |  | integer | no | `0` | 0 – 255 | تسامح الخلفية، من 0 إلى 255. |
| `background_mode` |  | enum (string) | no | `corners` | `edges`, `corners`, `midpoints` | مواقع البدء للملء الفيضي أثناء إزالة الخلفية. |

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.com/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.com/v1/requests/REQUEST_ID?wait=20" \
  -H "Authorization: Bearer $DOITONG_API_KEY"
```

### JavaScript

```javascript
const API = "https://api.doitong.com/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.com/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.com/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.com/v1/requests/req_8k2m4q7x1z9c3v5b6n0p2r4t",
    "cancel": "https://api.doitong.com/v1/requests/req_8k2m4q7x1z9c3v5b6n0p2r4t/cancel"
  }
}
```

### Output fields (`output`)

| Field | Type | Description |
|---|---|---|
| `pixel_scale` | integer | مقياس البكسل المكتشف في المدخل. |
| `palette` | array of string | لوحة ألوان الوسائط المعالجة. |
| `num_colors` | integer | عدد ألوان الوسائط المعالجة. |
| `images` | array of object {url, content_type, file_name, file_size, width, height} | فن البكسل المعالج والصور المكبرة، وكلاهما بتنسيق PNG. |

## 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.com/v1/models/image2pixel