# SDXL ControlNet Union · Image to Image API

> SDXL ya picha hadi picha yenye mwongozo wa ControlNet nyingi kutoka picha za udhibiti za mkao, kina, kingo, normal au ugawaji wa sehemu

Hubadilisha picha ya kuanzia kwa kutumia msingi wa SDXL unaoongozwa na kidokezo na picha za udhibiti za hiari za openpose, ramani ya kina, teed, canny, normal na ugawaji wa sehemu. Kila ingizo la udhibiti lina swichi yake ya preprocess. `strength` ni 0.05 hadi 1, `num_inference_steps` ni 1 hadi 70, na hadi picha 8 zinaweza kurejeshwa kwa ombi. LoRA na uwakilishi wa vekta vinaweza kutolewa, na tokeo ni jpeg au 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/sw/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 |  |  | Eleza picha unayotaka kuzalisha kwa maelezo mengi iwezekanavyo ili kupata matokeo bora. |
| `controlnet_conditioning_scale` |  | number | no | `0.5` | 0 – 1 | Nguvu ya conditioning inayotumiwa na ControlNet. |
| `image_url` | `image` | string (image URL) | yes |  |  | Picha ya kuanzia uzalishaji, iliyotolewa kupitia URL. |
| `negative_prompt` |  | string | no | `` |  | Kidokezo hasi kinachobainisha maelezo ya picha yasiyotakiwa. Kinaweza kuondoa rangi, vitu, mandhari au vipengele na sifa ndogo, kama vile masharubu, ukungu au mwonekano wa chini. |
| `image_size` |  | object {height, width} \| enum (string) \| null | no |  | `square_hd`, `square`, `portrait_4_3`, `portrait_16_9`, `landscape_4_3`, `landscape_16_9` | Vipimo vya tokeo, vinavyobainishwa kiotomatiki kutoka kwenye picha ya udhibiti wakati hakuna ukubwa uliotolewa. |
| `num_inference_steps` |  | integer | no | `35` | 1 – 70 | Idadi ya hatua za inference zitakazoendeshwa. |
| `guidance_scale` |  | number | no | `7.5` | 0 – 20 | Nguvu ya kufuata kidokezo kwa uteuzi wa picha, iliyoonyeshwa kama CFG (Classifier Free Guidance). |
| `strength` |  | number | no | `0.95` | 0.05 – 1 | Kiwango cha ufanano kati ya picha iliyozalishwa na picha ya awali. |
| `seed` |  | integer \| null | no |  |  | Mbegu ya kurudia picha katika Stable Diffusion. Picha inaweza kurudiwa kwa usahihi kila uendeshaji kwa kuweka toleo la modeli, kidokezo na mbegu bila kubadilika. |
| `num_images` | `num_outputs` | integer | no | `1` | 1 – 8 | Idadi ya picha iliyoombwa kwa uzalishaji. |
| `loras` |  | array of object {force, path, scale} | no | `[]` |  | Uzani wa LoRA uliochaguliwa kutumika, uliotolewa kama orodha. |
| `loras[].force` |  | boolean | no | `false` |  | Ikiwa matumizi ya embedding ni ya lazima: true huifanya kuwa ya lazima. |
| `loras[].path` |  | string | yes |  |  | Chanzo cha uzani wa LoRA, kinachotolewa kama kitambulishi cha modeli ya HF kinachoelekeza kwenye uzani wa safetensors au kama faili ya safetensors inayopatikana kwa URL au njia. |
| `loras[].scale` |  | number | no | `1` | 0 – 1 | Kizidishi kinachotumika kwenye uzito wa LoRA kabla ya kuunganishwa na modeli msingi. |
| `embeddings` |  | array of object {path, tokens} | no | `[]` |  | Embeddings zilizochaguliwa kutumika, zilizotolewa kama orodha. |
| `embeddings[].path` |  | string | yes |  |  | Uzani wa embedding uliobainishwa kwa URL au njia ya faili. |
| `embeddings[].tokens` |  | array of string \| null | no |  |  | Orodha ya tokeni za embedding, yenye majina ya kiotomatiki ikiachwa: kila vekta katika faili ya embedding hupokea tokeni kutoka kwenye mfuatano <s0>, <s1>, ... . |
| `enable_safety_checker` |  | boolean | no | `true` |  | Uanzishaji wa kikagua usalama; true huanzisha ukaguzi. |
| `safety_checker_version` |  | enum (string) | no | `v1` | `v1`, `v2` | Chagua kati ya kikagua usalama maalum cha ViT (`v2`) na kikagua cha CompVis (`v1`, chaguomsingi). |
| `expand_prompt` |  | boolean | no | `false` |  | Upanuzi wa kidokezo kwa vidokezo vya ziada, unaowashwa true inapowekwa. |
| `format` |  | enum (string) | no | `jpeg` | `jpeg`, `png` | Muundo wa faili wa picha inayotokana. |
| `request_id` |  | string | no | `` |  | Kitambulishi cha ombi cha kuhusisha jibu na ombi lililolitengeneza. |
| `preserve_aspect_ratio` |  | boolean | no | `false` |  | Weka true ili kuhifadhi uwiano wa vipengele wa picha inayozalishwa hata ukubwa wake unapozidi kikomo. Ili kupata uwiano sahihi kwa kupunguza baada ya uzalishaji, weka crop_output kuwa True. Thamani za upana au urefu zisizogawanyika kwa 32 bado huzungushwa hadi kizidishi cha 32 kilicho karibu zaidi, na ubadilishaji huu wa ukubwa hauhifadhi uwiano wa vipengele kwa chaguomsingi. |
| `crop_output` |  | boolean | no | `false` |  | Punguza tokeo lililozalishwa hadi uwiano sahihi wa vipengele wakati true. |
| `openpose_image_url` |  | string (image URL) \| null | no |  |  | URL inayoelekeza kwenye picha ya udhibiti. |
| `openpose_preprocess` |  | boolean | no | `true` |  | Hudhibiti uchakataji wa awali wa picha ya openpose. |
| `depth_image_url` |  | string (image URL) \| null | no |  |  | URL inayoelekeza kwenye picha ya udhibiti. |
| `depth_preprocess` |  | boolean | no | `true` |  | Hudhibiti uchakataji wa awali wa picha ya depth. |
| `teed_image_url` |  | string (image URL) \| null | no |  |  | URL inayoelekeza kwenye picha ya udhibiti. |
| `teed_preprocess` |  | boolean | no | `true` |  | Hudhibiti uchakataji wa awali wa picha ya teed. |
| `canny_image_url` |  | string (image URL) \| null | no |  |  | URL inayoelekeza kwenye picha ya udhibiti. |
| `canny_preprocess` |  | boolean | no | `true` |  | Hudhibiti uchakataji wa awali wa picha ya canny. |
| `normal_image_url` |  | string (image URL) \| null | no |  |  | URL inayoelekeza kwenye picha ya udhibiti. |
| `normal_preprocess` |  | boolean | no | `true` |  | Hudhibiti uchakataji wa awali wa picha ya normal. |
| `segmentation_image_url` |  | string (image URL) \| null | no |  |  | URL inayoelekeza kwenye picha ya udhibiti. |
| `segmentation_preprocess` |  | boolean | no | `true` |  | Hudhibiti uchakataji wa awali wa picha ya segmentation. |

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} | Maelezo ya faili za picha zilizozalishwa. |
| `timings` | object |  |
| `seed` | integer | Seed ya uzalishaji wa picha, ikijumuisha thamani iliyochaguliwa bila mpangilio ikiwa ingizo halikutoa seed; vinginevyo, hurudia thamani iliyotolewa. |
| `has_nsfw_concepts` | array of boolean | Huonyesha ikiwa dhana za NSFW zinaonekana katika picha zilizozalishwa. |
| `prompt` | string | Kidokezo kinachotumika wakati wa uzalishaji wa picha. |

## 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