# SDXL ControlNet Union · Image to Image API

> SDXL attēls-attēlā ar vairāku ControlNet vadību no pozas, dziļuma, malu, normal vai segmentēšanas kontroles attēliem

Pārveido sākuma attēlu, izmantojot SDXL bāzi, ko vada uzvedne un neobligāti kontroles attēli openpose, dziļuma karte, teed, canny, normal un segmentēšana. Katrai kontroles ievadei ir atsevišķs priekšapstrādes slēdzis. `strength` ir no 0.05 līdz 1, `num_inference_steps` — no 1 līdz 70, un vienā izsaukumā var atgriezt līdz 8 attēliem. Var norādīt LoRA un iegulumus, bet izvade ir jpeg vai 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.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/lv/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 |  |  | Aprakstiet ģenerējamo attēlu ar pēc iespējas lielāku detalizāciju, lai iegūtu labākos rezultātus. |
| `controlnet_conditioning_scale` |  | number | no | `0.5` | 0 – 1 | ControlNet lietotās nosacīšanas stiprums. |
| `image_url` | `image` | string (image URL) | yes |  |  | Sākuma attēls ģenerēšanai, kas nodrošināts ar URL. |
| `negative_prompt` |  | string | no | `` |  | Negatīva uzvedne ar nevēlamām attēla detaļām. Tā var izslēgt krāsas, objektus, ainavu vai smalkākas detaļas un īpašības, piemēram, ūsas, izplūdumu vai zemu izšķirtspēju. |
| `image_size` |  | object {height, width} \| enum (string) \| null | no |  | `square_hd`, `square`, `portrait_4_3`, `portrait_16_9`, `landscape_4_3`, `landscape_16_9` | Izvades izmēri, ko automātiski nosaka pēc vadības attēla, ja izmērs nav norādīts. |
| `num_inference_steps` |  | integer | no | `35` | 1 – 70 | Veicamo secināšanas darbību skaits. |
| `guidance_scale` |  | number | no | `7.5` | 0 – 20 | Uzvednes ievērošanas stiprums attēlu atlasē, izteikts kā CFG (Classifier Free Guidance). |
| `strength` |  | number | no | `0.95` | 0.05 – 1 | Ģenerētā attēla līdzības pakāpe ar sākotnējo attēlu. |
| `seed` |  | integer \| null | no |  |  | Sākumvērtība attēlu atkārtošanai Stable Diffusion. Attēlu var atkārtot pilnīgi identiski katrā palaišanas reizē, nemainot modeļa versiju, uzvedni un sākumvērtību. |
| `num_images` | `num_outputs` | integer | no | `1` | 1 – 8 | Ģenerēšanai pieprasītais attēlu skaits. |
| `loras` |  | array of object {force, path, scale} | no | `[]` |  | Lietošanai atlasītie LoRA svari, kas norādīti kā saraksts. |
| `loras[].force` |  | boolean | no | `false` |  | Vai iegulumu lietošana ir obligāta: true padara to obligātu. |
| `loras[].path` |  | string | yes |  |  | LoRA svaru avots, kas norādīts kā HF modeļa identifikators, kurš atrisina safetensors svarus, vai kā safetensors fails, kas atrodas pēc URL vai ceļa. |
| `loras[].scale` |  | number | no | `1` | 0 – 1 | Reizinātājs, kas tiek lietots LoRA svaram pirms tā sapludināšanas ar pamata modeli. |
| `embeddings` |  | array of object {path, tokens} | no | `[]` |  | Lietošanai atlasītie iegulumi, kas norādīti kā saraksts. |
| `embeddings[].path` |  | string | yes |  |  | Iegulumu svari, kas norādīti pēc URL vai faila ceļa. |
| `embeddings[].tokens` |  | array of string \| null | no |  |  | Ieguluma marķieru saraksts ar automātisku nosaukumu piešķiršanu, ja tas nav norādīts: katrs vektors ieguluma failā saņem marķieri no secības <s0>, <s1>, ... . |
| `enable_safety_checker` |  | boolean | no | `true` |  | Drošības pārbaudītāja aktivizēšana; true iespējo pārbaudi. |
| `safety_checker_version` |  | enum (string) | no | `v1` | `v1`, `v2` | Izvēlieties starp pielāgotu ViT drošības pārbaudītāju (`v2`) un CompVis pārbaudītāju (`v1`, noklusējums). |
| `expand_prompt` |  | boolean | no | `false` |  | Uzvednes paplašināšana ar papildu uzvednēm, aktivizēta, ja vērtība ir true. |
| `format` |  | enum (string) | no | `jpeg` | `jpeg`, `png` | Iegūtā attēla faila formāts. |
| `request_id` |  | string | no | `` |  | Pieprasījuma identifikators atbildes sasaistīšanai ar pieprasījumu, kas to izveidoja. |
| `preserve_aspect_ratio` |  | boolean | no | `false` |  | Iestatiet true, lai saglabātu ģenerētā attēla proporcijas arī tad, ja tā izmērs pārsniedz ierobežojumu. Lai pēc ģenerēšanas apgriešana nodrošinātu pareizās proporcijas, iestatiet crop_output uz True. Platuma vai augstuma vērtības, kas nedalās ar 32, joprojām tiek noapaļotas līdz tuvākajam 32 daudzkārtnim, un šī izmēra maiņa pēc noklusējuma nesaglabā proporcijas. |
| `crop_output` |  | boolean | no | `false` |  | Ja vērtība ir true, apgriež ģenerēto izvadi līdz pareizajām proporcijām. |
| `openpose_image_url` |  | string (image URL) \| null | no |  |  | URL, kas norāda uz vadības attēlu. |
| `openpose_preprocess` |  | boolean | no | `true` |  | Kontrolē openpose attēla priekšapstrādi. |
| `depth_image_url` |  | string (image URL) \| null | no |  |  | URL, kas norāda uz vadības attēlu. |
| `depth_preprocess` |  | boolean | no | `true` |  | Kontrolē dziļuma attēla priekšapstrādi. |
| `teed_image_url` |  | string (image URL) \| null | no |  |  | URL, kas norāda uz vadības attēlu. |
| `teed_preprocess` |  | boolean | no | `true` |  | Kontrolē teed attēla priekšapstrādi. |
| `canny_image_url` |  | string (image URL) \| null | no |  |  | URL, kas norāda uz vadības attēlu. |
| `canny_preprocess` |  | boolean | no | `true` |  | Kontrolē canny attēla priekšapstrādi. |
| `normal_image_url` |  | string (image URL) \| null | no |  |  | URL, kas norāda uz vadības attēlu. |
| `normal_preprocess` |  | boolean | no | `true` |  | Kontrolē normal attēla priekšapstrādi. |
| `segmentation_image_url` |  | string (image URL) \| null | no |  |  | URL, kas norāda uz vadības attēlu. |
| `segmentation_preprocess` |  | boolean | no | `true` |  | Kontrolē segmentēšanas attēla priekšapstrādi. |

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} | Ģenerēto attēlu failu informācija. |
| `timings` | object |  |
| `seed` | integer | Attēla ģenerēšanas sēkla, tostarp nejauši izvēlētā vērtība, ja ievadē sēkla nebija norādīta; pretējā gadījumā atkārto norādīto vērtību. |
| `has_nsfw_concepts` | array of boolean | Norāda, vai ģenerētajos attēlos parādās NSFW koncepcijas. |
| `prompt` | string | Attēla ģenerēšanas laikā izmantotā uzvedne. |

## 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.ru/v1/models/sdxl-controlnet-union/image-to-image