# AuraSR API

> يكبّر الصورة افتراضيًا بمقدار 4x، مع اختيار نقطة تحقق v1 أو v2 وبلاطات متداخلة اختيارية

يكبّر صورة من `image_url` باستخدام `upscale_factor`، وقيمته الافتراضية 4. يحدد `checkpoint` الإصدار v1 أو v2. يساعد تفعيل `overlapping_tiles` على إزالة الفواصل، لكنه يستغرق نحو ضعف الوقت. يعيد الصورة المكبّرة وأزمنة كل خطوة.

- Model id: `aura-sr`
- Made by: Open model
- Category: Upscale
- Output: image
- Charged by usage (up to $1.00 per run, typical $0.032; the rest of the hold is refunded): $0.001111 / second of processing time (cheapest run $0.032 (32 API credits))
- Run: `POST https://api.doitong.com/v1/run/aura-sr`
- Quote: `POST https://api.doitong.com/v1/quote/aura-sr`
- Docs page: https://doitong.com/ar/developers/models/aura-sr

## Pricing

Price: **$0.001111 / second of processing time**. Charged by actual usage at $0.001111 / second of processing time: up to $1.00 per run (the quote gives the maximum for your input), the rest is refunded automatically after the run. A typical run costs $0.032.

The full example input below holds up to $1.00 = 1000 API credits (up to 900 s of processing time × $0.001111) when it starts; a typical run costs $0.032 (32 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/aura-sr` 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 يشير إلى الصورة المراد تحسين دقتها. |
| `upscale_factor` |  | integer | no | `4` |  | معامل مقياس لتحسين الدقة. ستتوفر عوامل إضافية قريبًا. |
| `overlapping_tiles` |  | boolean | no | `false` |  | تحسين الدقة باستخدام بلاطات متداخلة: تستبدل true ضعف زمن الاستدلال بالمساعدة على إزالة الفواصل. |
| `checkpoint` |  | enum (string) | no | `v1` | `v1`, `v2` | نقطة التحقق المحددة لتحسين الدقة. ستتوفر نقاط تحقق إضافية قريبًا. |

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/aura-sr?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/aura-sr?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/aura-sr", 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": "aura-sr",
      "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": "aura-sr",
  "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": 1000,
    "usd": "1.00",
    "charged": 1000,
    "refunded": 0
  },
  "input": {
    "image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
  },
  "output": {
    "image": {
      "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
    },
    "timings": {}
  },
  "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 |
|---|---|---|
| `image` | object {url, content_type, file_name, file_size, width, height} | الصورة الناتجة عن تحسين الدقة. |
| `timings` | object | معلومات التوقيت لمراحل خط المعالجة الفردية. |

## Related models

- [Topaz Image Upscale](https://doitong.com/developers/models/topaz/upscale/image/precision.md) (`topaz/upscale/image/precision`): $0.08 / 24 megapixels of output image
- [Topaz Video Upscale](https://doitong.com/developers/models/topaz/upscale/video/precision.md) (`topaz/upscale/video/precision`): from $0.00667 / second of input video
- [Creative Upscaler](https://doitong.com/developers/models/creative-upscaler.md) (`creative-upscaler`): $0.00125 / second of processing time
- [DRCT-Super-Resolution](https://doitong.com/developers/models/drct-super-resolution.md) (`drct-super-resolution`): $0.0045 / megapixel of output image
- [Flashvsr](https://doitong.com/developers/models/flashvsr/upscale/video.md) (`flashvsr/upscale/video`): $0.0005 / megapixel of output video
- [Fooocus Upscale or Vary](https://doitong.com/developers/models/fooocus/upscale-or-vary.md) (`fooocus/upscale-or-vary`): $0.007 / image
- [Upscale Images](https://doitong.com/developers/models/esrgan.md) (`esrgan`): $0.000575 / second of processing time
- [Video Upscaler](https://doitong.com/developers/models/video-upscaler.md) (`video-upscaler`): $0.0008 / megapixel of output video

## Links

- HTML page: https://doitong.com/developers/models/aura-sr
- 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=aura-sr
- Errors: https://doitong.com/developers/docs/errors
- Model details as JSON: https://api.doitong.com/v1/models/aura-sr