# Flux Vision Upscaler API

> 影像放大器，支援 1-4x 倍率與創意程度控制，決定結果可偏離來源的幅度

放大由 `image_url` 提供的單張影像。`upscale_factor` 介於 1 到 4，`creativity` 介於 0 到 1；數值越高，輸出可偏離原始影像越多。也可設定 `guidance`、`steps`（4 到 50）和 `seed`。回應包含生成影像，以及處理期間產生的說明文字。

- 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/zh-TW/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 |  |  | 指向升頻來源圖像的 URL。 |
| `upscale_factor` |  | number | no | `2` | 1 – 4 | 放大倍數，範圍為 1 至 4×。 |
| `seed` |  | integer \| null | no |  |  | 放大種子值；除非提供值，否則會隨機選取。 |
| `creativity` |  | number | no | `0.3` | 0 – 1 | 創意設定，對應取樣去噪強度。提高此值可讓結果更偏離原始內容。 |
| `guidance` |  | number | no | `1` | 1 – 4 | 提示詞遵循設定，以 1 至 4 的 CFG/引導比例表示。 |
| `steps` |  | integer | no | `20` | 4 – 50 | 推論步數，範圍為 4 至 50。 |
| `enable_safety_checker` |  | boolean | no | `true` |  | 控制是否啟用安全性檢查。 |

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} | 指向生成圖像的 URL。 |
| `seed` | integer | 圖像生成期間使用的種子。 |
| `timings` | object | 各工作流程步驟的計時資訊。 |
| `caption` | string | VLM 產生、用於放大的描述文字。 |

## 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