# Clarity Upscaler API

> 扩散图像放大器，支持创意度和相似度控制，缩放范围为 1x 到 4x

将图像放大 1 到 4 倍。`creativity`（0 到 1）设置模型可创造多少细节，`resemblance`（0 到 1）促使结果保持接近原图。使用 `prompt` 和 `negative_prompt` 引导新增细节。可调整 `guidance_scale`、`num_inference_steps`（4 到 50）和种子。

- Model id: `clarity-upscaler`
- Made by: Clarity AI
- Category: Upscale
- Output: image
- Price per megapixel of your output image: $0.03 / megapixel of output image (cheapest run $0.03 (30 API credits))
- Run: `POST https://api.doitong.com/v1/run/clarity-upscaler`
- Quote: `POST https://api.doitong.com/v1/quote/clarity-upscaler`
- Docs page: https://doitong.com/zh/developers/models/clarity-upscaler

## Pricing

Price: **$0.03 / 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 $0.126 = 126 API credits (4.1943 MP output image × $0.03).

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/clarity-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 |  |  | 指向放大源图像的 URL。 |
| `prompt` |  | string | no | `masterpiece, best quality, highres` |  | 描述要生成的图像；请尽可能提供详细信息以获得最佳结果。 |
| `upscale_factor` |  | number | no | `2` | 1 – 4 | 用于放大的乘数。 |
| `negative_prompt` |  | string | no | `(worst quality, low quality, normal quality:2)` |  | 指定要从图像中排除的细节的负面提示词。 |
| `creativity` |  | number | no | `0.35` | 0 – 1 | 模型创造性，由采样去噪强度控制。较高值允许结果更大程度地偏离提示词。 |
| `resemblance` |  | number | no | `0.6` | 0 – 1 | 放大结果与源图像的相似度，由 ControlNet 强度控制。较高值会使模型保留更多原始图像内容。 |
| `guidance_scale` |  | number | no | `4` | 0 – 20 | 图像选择对提示词的遵循强度，以 CFG（Classifier Free Guidance）表示。 |
| `num_inference_steps` |  | integer | no | `18` | 4 – 50 | 要执行的推理步数。 |
| `seed` |  | integer \| null | no |  | ≥ 0 | 用于在 Stable Diffusion 中复现图像的种子。保持模型版本、提示词和种子不变，即可在每次运行中精确复现图像。 |
| `enable_safety_checker` |  | boolean | no | `true` |  | 安全检查器开关；将其设置为 false 可禁用检查。 |

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/clarity-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/clarity-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/clarity-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": "clarity-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": "clarity-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": 126,
    "usd": "0.126",
    "charged": 126,
    "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": {}
  },
  "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} | 指向生成图像的 URL。 |
| `seed` | integer | 图像生成过程中使用的种子。 |
| `timings` | object | 各个工作流步骤的计时信息。 |

## 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
- [Crystal Upscaler](https://doitong.com/developers/models/clarityai/crystal-upscaler.md) (`clarityai/crystal-upscaler`): $0.016 / megapixel of output image
- [Crystal Upscaler \[Video\]](https://doitong.com/developers/models/clarityai/crystal-video-upscaler.md) (`clarityai/crystal-video-upscaler`): from $0.10 / megapixel of output 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

## Links

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