# SDXL ControlNet Union · Image to Image API

> Imej-ke-imej SDXL dengan panduan berbilang ControlNet daripada imej kawalan pose, kedalaman, tepi, normal atau segmentasi

Mengubah imej permulaan menggunakan asas SDXL yang dipandu prompt dan imej kawalan pilihan untuk openpose, peta kedalaman, teed, canny, normal serta segmentasi. Setiap input kawalan mempunyai togol praproses sendiri. `strength` berjulat 0.05 hingga 1, `num_inference_steps` 1 hingga 70, dan sehingga 8 imej boleh dikembalikan setiap panggilan. LoRA dan pembenaman boleh dibekalkan, dengan output jpeg atau png.

- Model id: `sdxl-controlnet-union/image-to-image`
- Made by: Stability AI
- Category: Image Editing
- Output: image
- Estimated price per run: $0.048 / image (cheapest run $0.048 (48 API credits))
- Run: `POST https://api.doitong.com/v1/run/sdxl-controlnet-union/image-to-image`
- Quote: `POST https://api.doitong.com/v1/quote/sdxl-controlnet-union/image-to-image`
- Docs page: https://doitong.com/ms/developers/models/sdxl-controlnet-union/image-to-image

## Pricing

Price: **$0.048 / image**. Estimated from the typical cost of one run, per output (`num_images`). The quote is exactly what the run charges.

The full example input below costs $0.048 = 48 API credits (1 image × 48 API credits).

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/sdxl-controlnet-union/image-to-image` 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 |
|---|---|---|---|---|---|---|
| `prompt` |  | string | yes |  |  | Terangkan imej yang anda mahu dijana dengan sedetail mungkin untuk mendapatkan hasil terbaik. |
| `controlnet_conditioning_scale` |  | number | no | `0.5` | 0 – 1 | Kekuatan pengkondisian yang digunakan oleh ControlNet. |
| `image_url` | `image` | string (image URL) | yes |  |  | Imej permulaan untuk penjanaan, dibekalkan melalui URL. |
| `negative_prompt` |  | string | no | `` |  | Prompt negatif yang menyatakan butiran imej tidak diingini. Ia boleh mengecualikan warna, objek, pemandangan atau ciri dan kualiti halus seperti misai, kabur atau resolusi rendah. |
| `image_size` |  | object {height, width} \| enum (string) \| null | no |  | `square_hd`, `square`, `portrait_4_3`, `portrait_16_9`, `landscape_4_3`, `landscape_16_9` | Dimensi output, ditentukan secara automatik daripada imej kawalan apabila tiada saiz dibekalkan. |
| `num_inference_steps` |  | integer | no | `35` | 1 – 70 | Bilangan langkah inferens yang perlu dijalankan. |
| `guidance_scale` |  | number | no | `7.5` | 0 – 20 | Kekuatan pematuhan prompt untuk pemilihan imej, dinyatakan sebagai CFG (Classifier Free Guidance). |
| `strength` |  | number | no | `0.95` | 0.05 – 1 | Darjah keserupaan antara imej yang dijana dengan imej awal. |
| `seed` |  | integer \| null | no |  |  | Seed untuk menghasilkan semula imej dalam Stable Diffusion. Imej boleh dihasilkan semula dengan tepat pada setiap larian dengan mengekalkan versi model, prompt dan seed tanpa perubahan. |
| `num_images` | `num_outputs` | integer | no | `1` | 1 – 8 | Bilangan imej yang diminta untuk penjanaan. |
| `loras` |  | array of object {force, path, scale} | no | `[]` |  | Pemberat LoRA yang dipilih untuk digunakan, diberikan sebagai senarai. |
| `loras[].force` |  | boolean | no | `false` |  | Sama ada penggunaan embedding diwajibkan: true menjadikannya wajib. |
| `loras[].path` |  | string | yes |  |  | Sumber pemberat LoRA, diberikan sebagai pengecam model HF yang merujuk kepada pemberat safetensors atau sebagai fail safetensors yang ditemui melalui URL atau laluan. |
| `loras[].scale` |  | number | no | `1` | 0 – 1 | Pengganda yang digunakan pada berat LoRA sebelum digabungkan dengan model asas. |
| `embeddings` |  | array of object {path, tokens} | no | `[]` |  | Embedding yang dipilih untuk digunakan, diberikan sebagai senarai. |
| `embeddings[].path` |  | string | yes |  |  | Pemberat embedding yang ditentukan melalui URL atau laluan fail. |
| `embeddings[].tokens` |  | array of string \| null | no |  |  | Senarai token embedding, dengan penamaan automatik apabila diabaikan: setiap vektor dalam fail embedding menerima token daripada jujukan <s0>, <s1>, ... . |
| `enable_safety_checker` |  | boolean | no | `true` |  | Pengaktifan pemeriksa keselamatan; true mendayakan pemeriksaan. |
| `safety_checker_version` |  | enum (string) | no | `v1` | `v1`, `v2` | Pilih antara pemeriksa keselamatan ViT tersuai (`v2`) dan pemeriksa CompVis (`v1`, lalai). |
| `expand_prompt` |  | boolean | no | `false` |  | Pengembangan prompt dengan prompt tambahan, didayakan apabila true. |
| `format` |  | enum (string) | no | `jpeg` | `jpeg`, `png` | Format fail untuk imej yang terhasil. |
| `request_id` |  | string | no | `` |  | Pengecam permintaan untuk mengaitkan respons dengan permintaan yang menghasilkannya. |
| `preserve_aspect_ratio` |  | boolean | no | `false` |  | Tetapkan kepada true untuk mengekalkan nisbah aspek imej yang dijana walaupun saiznya melebihi had. Untuk mendapatkan perkadaran yang betul melalui pemangkasan selepas penjanaan, tetapkan crop_output kepada True. Nilai lebar atau tinggi yang tidak boleh dibahagi dengan 32 masih dibundarkan kepada gandaan 32 terdekat, dan saiz semula ini tidak mengekalkan nisbah aspek secara lalai. |
| `crop_output` |  | boolean | no | `false` |  | Pangkas output yang dijana kepada nisbah aspek yang betul apabila true. |
| `openpose_image_url` |  | string (image URL) \| null | no |  |  | URL yang menunjuk kepada imej kawalan. |
| `openpose_preprocess` |  | boolean | no | `true` |  | Mengawal prapemprosesan imej openpose. |
| `depth_image_url` |  | string (image URL) \| null | no |  |  | URL yang menunjuk kepada imej kawalan. |
| `depth_preprocess` |  | boolean | no | `true` |  | Mengawal prapemprosesan imej kedalaman. |
| `teed_image_url` |  | string (image URL) \| null | no |  |  | URL yang menunjuk kepada imej kawalan. |
| `teed_preprocess` |  | boolean | no | `true` |  | Mengawal prapemprosesan imej teed. |
| `canny_image_url` |  | string (image URL) \| null | no |  |  | URL yang menunjuk kepada imej kawalan. |
| `canny_preprocess` |  | boolean | no | `true` |  | Mengawal prapemprosesan imej canny. |
| `normal_image_url` |  | string (image URL) \| null | no |  |  | URL yang menunjuk kepada imej kawalan. |
| `normal_preprocess` |  | boolean | no | `true` |  | Mengawal prapemprosesan imej normal. |
| `segmentation_image_url` |  | string (image URL) \| null | no |  |  | URL yang menunjuk kepada imej kawalan. |
| `segmentation_preprocess` |  | boolean | no | `true` |  | Mengawal prapemprosesan imej pembahagian. |

File parameters (`image`, `openpose_image_url`, `depth_image_url`, `teed_image_url`, `canny_image_url`, `normal_image_url`, `segmentation_image_url`) 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",
  "prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field"
}
```

A fuller example:

```json
{
  "image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
  "prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field"
}
```

## 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/sdxl-controlnet-union/image-to-image?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",
    "prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field"
  }'

# 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/sdxl-controlnet-union/image-to-image?wait=20", {
  method: "POST",
  headers,
  body: JSON.stringify({
    "image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
    "prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field"
  }),
});
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",
    "prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field",
}

# Run the model; wait up to 20 s for the finished result in the same call
r = requests.post(API + "/run/sdxl-controlnet-union/image-to-image", 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": "sdxl-controlnet-union/image-to-image",
      "input": {
        "image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
        "prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field"
      },
      "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": "sdxl-controlnet-union/image-to-image",
  "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": 48,
    "usd": "0.048",
    "charged": 48,
    "refunded": 0
  },
  "input": {
    "image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
    "prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field"
  },
  "output": {
    "images": [
      {
        "url": "https://content.doitong.com/dev-api/2026/10/20/req_8k2m4q7x1z9c3v5b6n0p2r4t/0-output.jpeg",
        "width": 1024,
        "height": 768,
        "content_type": "image/jpeg"
      }
    ],
    "timings": {},
    "seed": 42,
    "has_nsfw_concepts": [
      false
    ],
    "prompt": "string"
  },
  "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 |
|---|---|---|
| `images` | array of object {url, width, height, content_type} | Maklumat fail untuk imej yang dijana. |
| `timings` | object |  |
| `seed` | integer | Seed penjanaan imej, termasuk nilai yang dipilih secara rawak jika input tidak membekalkan seed; jika tidak, nilai yang dibekalkan diulang. |
| `has_nsfw_concepts` | array of boolean | Menunjukkan sama ada konsep NSFW muncul dalam imej yang dijana. |
| `prompt` | string | Prompt yang digunakan semasa penjanaan imej. |

## Related models

- [SDXL ControlNet Union](https://doitong.com/developers/models/sdxl-controlnet-union.md) (`sdxl-controlnet-union`): $0.014 / image
- [SDXL ControlNet Union · Inpainting](https://doitong.com/developers/models/sdxl-controlnet-union/inpainting.md) (`sdxl-controlnet-union/inpainting`): $0.012 / image
- [FLUX.1 Kontext \[pro\]](https://doitong.com/developers/models/flux-pro/kontext.md) (`flux-pro/kontext`): $0.04 / image
- [GPT Image 2.5 Edit](https://doitong.com/developers/models/openai/gpt-image-2.5/flare/edit.md) (`openai/gpt-image-2.5/flare/edit`): from $0.12 / image
- [Nano Banana 2 Edit](https://doitong.com/developers/models/nano-banana-2/edit.md) (`nano-banana-2/edit`): from $0.06 / image
- [Nano Banana Pro Edit](https://doitong.com/developers/models/nano-banana-pro/edit.md) (`nano-banana-pro/edit`): from $0.15 / image
- [Qwen Image 3 Edit](https://doitong.com/developers/models/alibaba/qwen-image-3/edit.md) (`alibaba/qwen-image-3/edit`): from $0.04 / image
- [Seedream 5.0 Pro Edit](https://doitong.com/developers/models/bytedance/seedream/v5/pro/edit.md) (`bytedance/seedream/v5/pro/edit`): from $0.0675 / run of output image

## Links

- HTML page: https://doitong.com/developers/models/sdxl-controlnet-union/image-to-image
- 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=sdxl-controlnet-union/image-to-image
- Errors: https://doitong.com/developers/docs/errors
- Model details as JSON: https://api.doitong.com/v1/models/sdxl-controlnet-union/image-to-image