# SDXL ControlNet Union · Image to Image API

> SDXL larawan-to-larawan na may multi-ControlNet guidance mula sa pose, depth, edge, normal o segmentation control larawan

Binabago ang panimulang larawan gamit ang SDXL base na ginagabayan ng prompt at optional na control larawan para sa openpose, depth, teed, canny, normal at segmentation. May sariling preprocess toggle ang bawat control input. Gumagana ang `strength` mula 0.05 hanggang 1, ang `num_inference_steps` mula 1 hanggang 70, at maaaring magbalik ng hanggang 8 larawan bawat call. Maaaring magbigay ng LoRAs at embedding, at jpeg o png ang output.

- 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.ru/v1/run/sdxl-controlnet-union/image-to-image`
- Quote: `POST https://api.doitong.ru/v1/quote/sdxl-controlnet-union/image-to-image`
- Docs page: https://doitong.com/fil/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.ru/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 |  |  | Ilarawan ang larawang gusto mong mabuo, gamit ang pinakamaraming posibleng detalye para sa pinakamagandang resulta. |
| `controlnet_conditioning_scale` |  | number | no | `0.5` | 0 – 1 | Lakas ng conditioning na inilalapat ng ControlNet. |
| `image_url` | `image` | string (image URL) | yes |  |  | Panimulang larawan para sa pagbuo, na ibinigay sa pamamagitan ng URL. |
| `negative_prompt` |  | string | no | `` |  | Negative prompt na tumutukoy sa hindi gustong detalye ng larawan. Maaari nitong alisin ang mga kulay, bagay, tanawin, o maliliit na feature at katangian gaya ng bigote, blur, o mababang resolution. |
| `image_size` |  | object {height, width} \| enum (string) \| null | no |  | `square_hd`, `square`, `portrait_4_3`, `portrait_16_9`, `landscape_4_3`, `landscape_16_9` | Mga dimension ng output, na awtomatikong tinutukoy mula sa control larawan kapag walang ibinigay na size. |
| `num_inference_steps` |  | integer | no | `35` | 1 – 70 | Bilang ng inference step na isasagawa. |
| `guidance_scale` |  | number | no | `7.5` | 0 – 20 | Lakas ng pagsunod sa prompt para sa pagpili ng larawan, na ipinapahayag bilang CFG (Classifier Free Guidance). |
| `strength` |  | number | no | `0.95` | 0.05 – 1 | Antas ng pagkakahawig sa pagitan ng nabuong larawan at ng panimulang larawan. |
| `seed` |  | integer \| null | no |  |  | Seed para muling makabuo ng mga larawan sa Stable Diffusion. Eksaktong mauulit ang isang larawan sa bawat run kung hindi babaguhin ang bersyon ng modelo, prompt, at seed. |
| `num_images` | `num_outputs` | integer | no | `1` | 1 – 8 | Bilang ng larawang hinihingi para sa pagbuo. |
| `loras` |  | array of object {force, path, scale} | no | `[]` |  | Mga LoRA weight na piniling gamitin, na ibinibigay bilang listahan. |
| `loras[].force` |  | boolean | no | `false` |  | Kung mandatory ang paggamit ng embedding: ginagawa itong compulsory ng true. |
| `loras[].path` |  | string | yes |  |  | Pinagmulan ng LoRA weight, ibinibigay bilang HF modelo identifier na tumutukoy sa safetensors weights o bilang safetensors file na matatagpuan sa pamamagitan ng URL o path. |
| `loras[].scale` |  | number | no | `1` | 0 – 1 | Multiplier na inilalapat sa LoRA weight bago ito i-merge sa base modelo. |
| `embeddings` |  | array of object {path, tokens} | no | `[]` |  | Mga embedding na piniling gamitin, na ibinibigay bilang listahan. |
| `embeddings[].path` |  | string | yes |  |  | Mga embedding weight na tinukoy sa pamamagitan ng URL o file path. |
| `embeddings[].tokens` |  | array of string \| null | no |  |  | Listahan ng embedding token, na may awtomatikong pagbibigay ng pangalan kapag hindi ito ibinigay: bawat vector sa embedding file ay tatanggap ng token mula sa sequence na <s0>, <s1>, ... . |
| `enable_safety_checker` |  | boolean | no | `true` |  | Pag-activate ng safety checker; pinapagana ng true ang checking. |
| `safety_checker_version` |  | enum (string) | no | `v1` | `v1`, `v2` | Pumili sa pagitan ng custom na ViT safety checker (`v2`) at ng CompVis checker (`v1`, ang default). |
| `expand_prompt` |  | boolean | no | `false` |  | Pagpapalawak ng prompt gamit ang mga karagdagang prompt, pinapagana kapag true. |
| `format` |  | enum (string) | no | `jpeg` | `jpeg`, `png` | File format ng resultang larawan. |
| `request_id` |  | string | no | `` |  | Identifier ng kahilingan para maiugnay ang response sa kahilingang gumawa nito. |
| `preserve_aspect_ratio` |  | boolean | no | `false` |  | Itakda sa true upang mapanatili ang aspect ratio ng nabuong larawan kahit lumampas ang size nito sa limit. Para makuha ang tamang proporsiyon sa pamamagitan ng pag-crop pagkatapos ng pagbuo, itakda ang crop_output sa True. Ang mga width o height value na hindi divisible ng 32 ay ira-round pa rin sa pinakamalapit na multiple ng 32, at hindi pinapanatili ng resizing na ito ang aspect ratio bilang default. |
| `crop_output` |  | boolean | no | `false` |  | I-crop ang nabuong output sa tamang aspect ratio kapag true. |
| `openpose_image_url` |  | string (image URL) \| null | no |  |  | URL na tumutukoy sa control larawan. |
| `openpose_preprocess` |  | boolean | no | `true` |  | Kinokontrol ang preprocessing ng openpose larawan. |
| `depth_image_url` |  | string (image URL) \| null | no |  |  | URL na tumutukoy sa control larawan. |
| `depth_preprocess` |  | boolean | no | `true` |  | Kinokontrol ang preprocessing ng depth larawan. |
| `teed_image_url` |  | string (image URL) \| null | no |  |  | URL na tumutukoy sa control larawan. |
| `teed_preprocess` |  | boolean | no | `true` |  | Kinokontrol ang preprocessing ng teed larawan. |
| `canny_image_url` |  | string (image URL) \| null | no |  |  | URL na tumutukoy sa control larawan. |
| `canny_preprocess` |  | boolean | no | `true` |  | Kinokontrol ang preprocessing ng canny larawan. |
| `normal_image_url` |  | string (image URL) \| null | no |  |  | URL na tumutukoy sa control larawan. |
| `normal_preprocess` |  | boolean | no | `true` |  | Kinokontrol ang preprocessing ng normal larawan. |
| `segmentation_image_url` |  | string (image URL) \| null | no |  |  | URL na tumutukoy sa control larawan. |
| `segmentation_preprocess` |  | boolean | no | `true` |  | Kinokontrol ang preprocessing ng segmentation larawan. |

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.ru/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.ru/v1/requests/REQUEST_ID?wait=20" \
  -H "Authorization: Bearer $DOITONG_API_KEY"
```

### JavaScript

```javascript
const API = "https://api.doitong.ru/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.ru/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.ru/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.ru/v1/requests/req_8k2m4q7x1z9c3v5b6n0p2r4t",
    "cancel": "https://api.doitong.ru/v1/requests/req_8k2m4q7x1z9c3v5b6n0p2r4t/cancel"
  }
}
```

### Output fields (`output`)

| Field | Type | Description |
|---|---|---|
| `images` | array of object {url, width, height, content_type} | Impormasyon ng file para sa mga nabuong larawan. |
| `timings` | object |  |
| `seed` | integer | Seed para sa pagbuo ng larawan, kasama ang random na napiling value kung walang seed sa input; kung hindi, inuulit nito ang ibinigay na value. |
| `has_nsfw_concepts` | array of boolean | Nagsasaad kung may mga konseptong NSFW sa nabuong mga larawan. |
| `prompt` | string | Prompt na ginagamit sa pagbuo ng larawan. |

## Related models

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- [Nano Banana 2 Edit](https://doitong.com/developers/models/nano-banana-2/edit.md) (`nano-banana-2/edit`): from $0.06 / image
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- [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.ru/v1/models/sdxl-controlnet-union/image-to-image