# Topaz Video Upscale API

> Topaz Labs video mērogotājs un atjaunotājs līdz 4x ar neobligātu kadru ātruma pārveidošanu.

Palielina un atjauno video ar Topaz Proteus, Iris, Dione, Artemis, Gaia, Rhea un Theia modeļiem, sākot no veca DV un TV materiāla līdz animācijai. Pieejamas detaļu atjaunošanas, trokšņu, halo, kompresijas un graudainuma vadīklas, kā arī neobligāts mērķa kadru ātrums no 16 līdz 60 fps.

- Model id: `topaz/upscale/video/precision`
- Made by: Topaz Labs
- Category: Upscale
- Output: video
- Price per second of your input video: from $0.00667 / second of input video (cheapest run $0.10 (100 API credits))
- Run: `POST https://api.doitong.com/v1/run/topaz/upscale/video/precision`
- Quote: `POST https://api.doitong.com/v1/quote/topaz/upscale/video/precision`
- Docs page: https://doitong.com/lv/developers/models/topaz/upscale/video/precision

## Pricing

Price: **from $0.00667 / second of input video** (the lowest tier; the quote prices your exact input). Price per second of your input video: 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.

Price by tier:

- 720p: $0.00667 / second of input video
- 1080p: $0.0133 / second of input video
- 720p, frame rate: $0.0133 / second of input video
- 1080p, frame rate: $0.0267 / second of input video
- 4K: $0.0517 / second of input video
- above 4K: $0.0919 / second of input video
- 4K, frame rate: $0.1033 / second of input video
- above 4K, frame rate: $0.1837 / second of input video

The full example input below costs $0.60 = 600 API credits (10 s input video × $0.051667 (4K), rounded up to the next $0.10).

Prices are the upstream price in USD, paid from the API balance in API credits (1000 API credits = 1 USD). `POST https://api.doitong.com/v1/quote/topaz/upscale/video/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 |
|---|---|---|---|---|---|---|
| `video_url` | `video` | string (video URL) | yes |  |  | Avota video URL izšķirtspējas palielināšanai. |
| `model` |  | enum (string) | no | `Proteus` | `Proteus`, `Proteus Natural`, `Iris`, `Iris Low Quality`, `Dione DV`, `Dione TV`, `Dione Robust`, `Dione Dehalo`, `Dione Robust Dehalo`, `Artemis High Quality`, `Artemis Medium Quality`, `Artemis Low Quality`, `Artemis Strong Halo`, `Artemis Medium Halo`, `Artemis Aliasing & Moire`, `Gaia HQ`, `Gaia CG`, `Gaia 2`, `Rhea`, `Theia Fine Tune Detail`, `Theia Fine Tune Fidelity` | Negeneratīvs precīzās uzlabošanas modelis: Theia nodrošina manuālu uzticamības un detaļu vadību, bet Rhea maksimāli palielina smalkās detaļas ar iekšēju 4x caurlaidi. Atjaunošanas uzdevumiem izmantojiet Artemis bojāta materiāla asināšanai un trokšņu samazināšanai, Dione mantota vai rindpārleces materiāla deinterlacingam vai Iris seju atjaunošanai. Lielākajai daļai reālās pasaules materiāla ir piemērots Proteus, bet Proteus Natural piedāvā maigāku alternatīvu; Gaia uzlabo augstas kvalitātes materiālu un CG, savukārt Gaia 2 nodrošina 2x palielināšanu animācijai. |
| `upscale_factor` |  | number | no | `2` | 1 – 4 | Video mērogošanas reizinātājs; vērtība 2.0 dubulto abus kadra izmērus. |
| `target_fps` |  | integer \| null | no |  | 16 – 60 | Mērķa izvades kadru nomaiņas ātrums; avota FPS saglabāšana atspējo interpolāciju, un to iespējo tikai atšķirīgs FPS. |
| `compression` |  | number \| null | no |  | 0 – 1 | Kompresijas artefaktu noņemšanas stiprums no 0.0 līdz 1.0. Noklusējumu nosaka modelis. |
| `noise` |  | number \| null | no |  | 0 – 1 | Trokšņu samazināšanas stiprums no 0.0 līdz 1.0. Noklusējumu nosaka modelis. |
| `halo` |  | number \| null | no |  | 0 – 1 | Halo samazināšanas stiprums no 0.0 līdz 1.0. Noklusējumu nosaka modelis. |
| `grain` |  | number \| null | no |  | 0 – 0.1 | Filmas grauda daudzums no 0.0 līdz 0.1. Noklusējums ir atkarīgs no modeļa. |
| `recover_detail` |  | number \| null | no |  | 0 – 1 | Oriģinālo detaļu atjaunošanas stiprums no 0.0 līdz 1.0. Palielinot šo vērtību, tiek saglabāts vairāk avota detaļu. |
| `H264_output` |  | boolean | no | `false` |  | Nosaka, vai izvades video izmanto H264 kodējumu noklusējuma H265 vietā. |

File parameters (`video`) 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
{
  "video": "https://content.doitong.com/video/showcase/v3/gen_fox_cartoon.mp4"
}
```

A fuller example:

```json
{
  "video": "https://content.doitong.com/video/showcase/v3/gen_fox_cartoon.mp4",
  "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.com/v1/run/topaz/upscale/video/precision?wait=20" \
  -H "Authorization: Bearer $DOITONG_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "video": "https://content.doitong.com/video/showcase/v3/gen_fox_cartoon.mp4",
    "upscale_factor": 2
  }'

# 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/topaz/upscale/video/precision?wait=20", {
  method: "POST",
  headers,
  body: JSON.stringify({
    "video": "https://content.doitong.com/video/showcase/v3/gen_fox_cartoon.mp4",
    "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.com/v1"
headers = {"Authorization": "Bearer " + os.environ["DOITONG_API_KEY"]}

payload = {
    "video": "https://content.doitong.com/video/showcase/v3/gen_fox_cartoon.mp4",
    "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/video/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.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": "topaz/upscale/video/precision",
      "input": {
        "video": "https://content.doitong.com/video/showcase/v3/gen_fox_cartoon.mp4",
        "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/video/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": 600,
    "usd": "0.60",
    "charged": 600,
    "refunded": 0
  },
  "input": {
    "video": "https://content.doitong.com/video/showcase/v3/gen_fox_cartoon.mp4",
    "upscale_factor": 2
  },
  "output": {
    "video": {
      "url": "https://content.doitong.com/dev-api/2026/10/20/req_8k2m4q7x1z9c3v5b6n0p2r4t/0-output.mp4",
      "content_type": "video/mp4",
      "file_name": "0-output.mp4",
      "file_size": 1843200
    }
  },
  "outputs": [
    {
      "type": "video",
      "url": "https://content.doitong.com/dev-api/2026/10/20/req_8k2m4q7x1z9c3v5b6n0p2r4t/0-output.mp4",
      "content_type": "video/mp4",
      "file_size": 1843200,
      "width": 1280,
      "height": 720,
      "duration": 5,
      "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 |
|---|---|---|
| `video` | object {url, content_type, file_name, file_size} | Video fails, kas izveidots, palielinot izšķirtspēju. |

## Related models

- [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
- [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 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
- [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/video/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/video/precision
- Errors: https://doitong.com/developers/docs/errors
- Model details as JSON: https://api.doitong.com/v1/models/topaz/upscale/video/precision