# Topaz Image Upscale API

> Augmentar la resoluziun da maletgs da Topaz Labs cun restituziun da fatschas, denoising e meglieraziun da la nettezza, fin a 4x.

Engrondescha ina fotografia u ina visualisaziun fin a quatter giadas cun models Topaz adattads a differentas funtaunas: fotografias standard, alta fidaivladad, funtaunas a bassa resoluziun, CGI, text e fatschas. Meglieraziun facultativa da fatschas, eliminaziun da ramur, scharpaziun e reparaziun da cumpressiun; sortida JPEG u PNG.

- Model id: `topaz/upscale/image/precision`
- Made by: Topaz Labs
- Category: Upscale
- Output: image
- Price per 24 megapixels of your output image: $0.08 / 24 megapixels of output image (cheapest run $0.08 (80 API credits))
- Run: `POST https://api.doitong.ru/v1/run/topaz/upscale/image/precision`
- Quote: `POST https://api.doitong.ru/v1/quote/topaz/upscale/image/precision`
- Docs page: https://doitong.com/rm/developers/models/topaz/upscale/image/precision

## Pricing

Price: **$0.08 / 24 megapixels of output image**. Price per 24 megapixels of your output image: the input files are measured before the run (length, resolution, frame rate). The quote measures the same files and is exactly what the run charges.

The full example input below costs $0.08 = 80 API credits (1 × 24 MP output image × $0.08).

Prices are the upstream price in USD, paid from the API balance in API credits (1000 API credits = 1 USD). `POST https://api.doitong.ru/v1/quote/topaz/upscale/image/precision` with the same JSON body returns the exact price for that input; the quote is exactly what a run charges. 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 |  | ≥ 1 characters | URL dal maletg da funtauna per l'agrandiment. |
| `model` |  | enum (string) | no | `Standard V2` | `Standard V2`, `High Fidelity V3`, `High Fidelity V2`, `Low Resolution V2`, `CGI`, `Text Refine`, `Faces` | Selecziun dal model d'agrandiment precis: Faces utilisescha Face Recovery 3 per la reconstrucziun da fatschas, Text Refine mantegna text e furmas netgs, e CGI è adattà per ovras d'art e graficas renderizadas. Per fotografias cuvra Standard V2 la gronda part dals cas, High Fidelity V3 e High Fidelity V2 mantegnan detagls fotografads professionalmain, e Low Resolution V2 reconstruescha funtaunas comprimidas. |
| `upscale_factor` |  | number | no | `2` | 1 – 4 | Multiplicader d'agrandiment dal maletg; per exempel, 2.0 dublegia omaduas dimensiuns. |
| `crop_to_fill` |  | boolean | no | `false` |  |  |
| `output_format` |  | enum (string) | no | `jpeg` | `jpeg`, `png` | Format utilisà per il maletg agrandì. |
| `subject_detection` |  | enum (string) | no | `All` | `All`, `Foreground`, `Background` | Configuraziun da la detecziun dal tema utilisada durant il meglierament dal maletg. |
| `face_enhancement` |  | boolean | no | `true` |  | Activaziun dal meglierament da fatschas per il maletg. |
| `face_enhancement_creativity` |  | number | no | `0` | 0 – 1 | Controlla da la creativitad dal meglierament da fatschas, activa mo durant che quest meglierament è activ: fixescha 1.0 per creativitad maximala u 0.0 per nagina. |
| `face_enhancement_strength` |  | number | no | `0.8` | 0 – 1 | Controlla da la forza dal meglierament da fatschas, activa mo durant che quest meglierament è activ: fixescha 1.0 per il meglierament maximal u 0.0 per nagin. |
| `sharpen` |  | number \| null | no |  | 0 – 1 | Intensitad dal sclariment da 0.0 fin a 1.0, cun in standard dependent dal model. |
| `denoise` |  | number \| null | no |  | 0 – 1 | Intensitad da la reducziun dal ramur da 0.0 fin a 1.0, cun in standard dependent dal model. |
| `fix_compression` |  | number \| null | no |  | 0 – 1 | Intensitad da la schliaziun dals artifacts da compressiun da 0.0 fin a 1.0; betg disponibla cun CGI. |
| `strength` |  | number \| null | no |  | 0.01 – 1 | Intensitad dal meglierament da 0.01 fin a 1.0, sustegnida mo da Text Refine. |

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": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
  "upscale_factor": 2
}
```

## 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/topaz/upscale/image/precision?wait=20" \
  -H "Authorization: Bearer $DOITONG_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "image": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
    "upscale_factor": 2
  }'

# 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/topaz/upscale/image/precision?wait=20", {
  method: "POST",
  headers,
  body: JSON.stringify({
    "image": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
    "upscale_factor": 2
  }),
});
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": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
    "upscale_factor": 2,
}

# Run the model; wait up to 20 s for the finished result in the same call
r = requests.post(API + "/run/topaz/upscale/image/precision", 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": "topaz/upscale/image/precision",
      "input": {
        "image": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
        "upscale_factor": 2
      },
      "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": "topaz/upscale/image/precision",
  "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": 80,
    "usd": "0.08",
    "charged": 80,
    "refunded": 0
  },
  "input": {
    "image": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
    "upscale_factor": 2
  },
  "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
    }
  },
  "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 |
|---|---|---|
| `image` | object {url, content_type, file_name, file_size} | Maletg produciut tras l'engrondiment. |

## Related models

- [Topaz Upscale Image Creative](https://doitong.com/developers/models/topaz/upscale/image/creative.md) (`topaz/upscale/image/creative`): $0.08 / 2 megapixels of output image
- [Topaz Upscale Image Generative](https://doitong.com/developers/models/topaz/upscale/image/generative.md) (`topaz/upscale/image/generative`): from $0.01 / megapixel of output image
- [Topaz Upscale Image Transparent](https://doitong.com/developers/models/topaz/upscale/image/transparent.md) (`topaz/upscale/image/transparent`): $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
- [Topaz Upscale Video Creative](https://doitong.com/developers/models/topaz/upscale/video/creative.md) (`topaz/upscale/video/creative`): from $0.30 / second of input video
- [Topaz Upscale Video Generative](https://doitong.com/developers/models/topaz/upscale/video/generative.md) (`topaz/upscale/video/generative`): from $0.06 / second of input video
- [AuraSR](https://doitong.com/developers/models/aura-sr.md) (`aura-sr`): $0.001111 / second of processing time
- [Bria Increase Resolution](https://doitong.com/developers/models/bria/increase-resolution.md) (`bria/increase-resolution`): $0.04 / image

## Links

- HTML page: https://doitong.com/developers/models/topaz/upscale/image/precision
- 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=topaz/upscale/image/precision
- Errors: https://doitong.com/developers/docs/errors
- Model details as JSON: https://api.doitong.ru/v1/models/topaz/upscale/image/precision