# Flux Vision Upscaler API

> 1～4倍の倍率と、元画像からどの程度変化させるかを制御する創造性設定を備えた画像アップスケーラーです

`image_url`で指定した画像を拡大します。`upscale_factor`は1～4、`creativity`は0～1で、高い値ほど出力が元画像から大きく変化します。`guidance`、`steps`（4～50）、`seed`も設定できます。レスポンスには生成画像と、処理中に生成されたキャプションが含まれます。

- Model id: `flux-vision-upscaler`
- Made by: Black Forest Labs
- Category: Upscale
- Output: image
- Price per megapixel of your output image: $0.10 / megapixel of output image (cheapest run $0.10 (100 API credits))
- Run: `POST https://api.doitong.com/v1/run/flux-vision-upscaler`
- Quote: `POST https://api.doitong.com/v1/quote/flux-vision-upscaler`
- Docs page: https://doitong.com/ja/developers/models/flux-vision-upscaler

## Pricing

Price: **$0.10 / megapixel of output image**. Price per megapixel 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 $0.42 = 420 API credits (4.1943 MP output image × $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/flux-vision-upscaler` 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 |  |  | アップスケーリング元の画像を指すURL。 |
| `upscale_factor` |  | number | no | `2` | 1 – 4 | 1〜4×のアップスケーリング倍率。 |
| `seed` |  | integer \| null | no |  |  | アップスケーリング用のシード。値を指定しない場合はランダムに選択されます。 |
| `creativity` |  | number | no | `0.3` | 0 – 1 | サンプリングのデノイズ強度に対応する創造性設定。値を上げると元画像からの変更幅が大きくなります。 |
| `guidance` |  | number | no | `1` | 1 – 4 | 1〜4のCFG／ガイダンススケールで指定するプロンプト準拠度。 |
| `steps` |  | integer | no | `20` | 4 – 50 | 4〜50の推論ステップ数。 |
| `enable_safety_checker` |  | boolean | no | `true` |  | 安全性チェックを有効にするかどうかを制御します。 |

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/flux-vision-upscaler?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/flux-vision-upscaler?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/flux-vision-upscaler", 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": "flux-vision-upscaler",
      "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": "flux-vision-upscaler",
  "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": 420,
    "usd": "0.42",
    "charged": 420,
    "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
    },
    "seed": 42,
    "timings": {},
    "caption": "A highly detailed upscaled photograph featuring sharp edges and enhanced textures. The image shows improved clarity in fine details with natural color preservation and minimal artifacts."
  },
  "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} | 生成された画像を指すURL。 |
| `seed` | integer | 画像生成中に使用するシード。 |
| `timings` | object | ワークフローの各ステップのタイミング情報。 |
| `caption` | string | アップスケーリングに使用するVLM生成のキャプション。 |

## 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
- [Flux Video Upscale](https://doitong.com/developers/models/blackforestlabs/flux-video-upscale.md) (`blackforestlabs/flux-video-upscale`): from $0.084 / 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
- [Bytedance Upscaler Upscale Video](https://doitong.com/developers/models/bytedance-upscaler/upscale/video.md) (`bytedance-upscaler/upscale/video`): from $0.0072 / second of input video
- [Chrono Edit Lora Gallery · Upscaler](https://doitong.com/developers/models/chrono-edit-lora-gallery/upscaler.md) (`chrono-edit-lora-gallery/upscaler`): $0.03 / image
- [Clarity Upscaler](https://doitong.com/developers/models/clarity-upscaler.md) (`clarity-upscaler`): $0.03 / megapixel of output image

## Links

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