# Image2Pixel API

> رنگوں کے مجموعے کی حدود، پیمانے کی شناخت اور اختیاری شفاف پس منظر کے ساتھ تصاویر کو صاف پکسل آرٹ میں تبدیل کرتا ہے

ان پٹ تصویر کو پکسل آرٹ میں تبدیل کرکے پراسیس شدہ تصویر اور پیمانہ شدہ ورژن PNG فائلوں کے طور پر واپس کرتا ہے۔ `max_colors` (1 سے 256) سے رنگوں کا مجموعہ محدود کریں، ہیکس رنگوں کا `fixed_palette` دیں، یا `scale` کو 1 سے 64 تک مقرر کریں۔ متعدد چھوٹا کرنے کے طریقے، صفائی کے اختیارات اور پس منظر ہٹانے کے طریقے دستیاب ہیں۔ جواب رنگوں کی تعداد، رنگوں کے مجموعے اور شناخت شدہ پکسل پیمانے کی بھی اطلاع دیتا ہے۔

- 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/ur/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 |  |  | بہتر pixel art میں تبدیل کرنے کے لیے ماخذ تصویر کا URL۔ |
| `max_colors` |  | integer \| null | no | `32` | 1 – 256 | آؤٹ پٹ پیلیٹ میں رنگوں کی زیادہ سے زیادہ تعداد؛ None لامحدود رنگوں کی اجازت دیتا ہے۔ |
| `auto_color_detect` |  | boolean | no | `false` |  | بہترین رنگوں کی تعداد کا خودکار انتخاب فعال کرتا ہے۔ |
| `fixed_palette` |  | array of string \| null | no |  |  | اختیاری پہلے سے متعین پیلیٹ، hex strings کے ساتھ، جیسے ['#000000', '#ffffff']۔ |
| `detect_method` |  | enum (string) | no | `auto` | `auto`, `runs`, `edge` | اسکیل کی شناخت کے لیے استعمال ہونے والا طریقہ۔ |
| `scale` |  | integer \| null | no |  | 1 – 64 | لاگو کیا جانے والا pixel scale؛ None خودکار شناخت فعال کرتا ہے۔ |
| `downscale_method` |  | enum (string) | no | `dominant` | `dominant`, `median`, `mode`, `mean`, `content-adaptive` | pixel art میں downscale کرنے کے لیے استعمال ہونے والا طریقہ۔ |
| `trim_borders` |  | boolean | no | `false` |  | تصویر کے کناروں کی تراش خراش کو کنٹرول کرتا ہے۔ |
| `transparent_background` |  | boolean | no | `false` |  | شفافیت کے ذریعے پس منظر ہٹانا: تصویر کی اصلاح مکمل ہونے کے بعد، تصویر کے کنارے سے جڑے رنگین خطے شفاف ہو جاتے ہیں۔ |
| `cleanup_morph` |  | boolean | no | `false` |  | morphological operations کے ذریعے شور ہٹانا۔ |
| `cleanup_jaggy` |  | boolean | no | `false` |  | الگ تھلگ ترچھے pixels ہٹا کر ناہموار کناروں کی صفائی۔ |
| `snap_grid` |  | boolean | no | `true` |  | آؤٹ پٹ کو pixel grid پر منطبق کرتا ہے۔ |
| `alpha_threshold` |  | integer | no | `128` | 0 – 255 | alpha کو binary بنانے کی حد، 0 سے 255 تک۔ |
| `dominant_color_threshold` |  | number | no | `0.05` | 0 – 1 | غالب رنگوں کی حد، 0.0 سے 1.0 تک۔ |
| `background_tolerance` |  | integer | no | `0` | 0 – 255 | پس منظر کے لیے برداشت، 0 سے 255 تک۔ |
| `background_mode` |  | enum (string) | no | `corners` | `edges`, `corners`, `midpoints` | پس منظر ہٹاتے وقت flood-fill کے ابتدائی مقامات۔ |

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 | ان پٹ میں شناخت کیا گیا pixel scale۔ |
| `palette` | array of string | پروسیس شدہ میڈیا کی رنگوں کی پیلیٹ۔ |
| `num_colors` | integer | پروسیس شدہ میڈیا میں رنگوں کی تعداد۔ |
| `images` | array of object {url, content_type, file_name, file_size, width, height} | پروسیس شدہ pixel art اور scaled تصاویر، دونوں PNG فارمیٹ میں۔ |

## 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