# SDXL ControlNet Union · Image to Image API

> SDXL gambar-ke-gambar dengan panduan multi-ControlNet dari gambar kontrol pose, peta kedalaman, edge, normal, atau segmentasi

Mengubah gambar awal menggunakan basis SDXL yang dipandu oleh prompt dan gambar kontrol opsional untuk openpose, peta kedalaman, teed, canny, normal, dan segmentasi. Setiap input kontrol memiliki toggle praproses sendiri. `strength` berjalan dari 0.05 hingga 1, `num_inference_steps` dari 1 hingga 70, dan hingga 8 gambar dapat dikembalikan per panggilan. LoRA dan embedding dapat diberikan, dan output berupa 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.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/id/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 |  |  | Jelaskan gambar yang ingin dibuat sedetail mungkin untuk memperoleh hasil terbaik. |
| `controlnet_conditioning_scale` |  | number | no | `0.5` | 0 – 1 | Kekuatan conditioning yang diterapkan oleh ControlNet. |
| `image_url` | `image` | string (image URL) | yes |  |  | Gambar awal untuk pembuatan, diberikan melalui URL. |
| `negative_prompt` |  | string | no | `` |  | Negative prompt yang menentukan detail gambar yang tidak diinginkan. Prompt ini dapat mengecualikan warna, objek, pemandangan, atau fitur dan kualitas yang lebih halus, seperti kumis, buram, 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 otomatis dari gambar kontrol saat ukuran tidak diberikan. |
| `num_inference_steps` |  | integer | no | `35` | 1 – 70 | Jumlah langkah inferensi yang dijalankan. |
| `guidance_scale` |  | number | no | `7.5` | 0 – 20 | Kekuatan kepatuhan terhadap prompt untuk pemilihan gambar, dinyatakan sebagai CFG (Classifier Free Guidance). |
| `strength` |  | number | no | `0.95` | 0.05 – 1 | Tingkat kemiripan antara gambar yang dihasilkan dan gambar awal. |
| `seed` |  | integer \| null | no |  |  | Seed untuk mereproduksi gambar dalam Stable Diffusion. Gambar dapat direproduksi secara persis pada setiap proses dengan mempertahankan versi model, prompt, dan seed. |
| `num_images` | `num_outputs` | integer | no | `1` | 1 – 8 | Jumlah gambar yang diminta untuk dibuat. |
| `loras` |  | array of object {force, path, scale} | no | `[]` |  | Bobot LoRA yang dipilih untuk digunakan, diberikan sebagai daftar. |
| `loras[].force` |  | boolean | no | `false` |  | Apakah penggunaan embedding wajib: true menjadikannya wajib. |
| `loras[].path` |  | string | yes |  |  | Sumber bobot LoRA, diberikan sebagai pengenal model HF yang mengarah ke bobot safetensors atau sebagai file safetensors yang ditemukan melalui URL atau path. |
| `loras[].scale` |  | number | no | `1` | 0 – 1 | Pengali yang diterapkan pada bobot LoRA sebelum digabungkan ke model dasar. |
| `embeddings` |  | array of object {path, tokens} | no | `[]` |  | Embedding yang dipilih untuk digunakan, diberikan sebagai daftar. |
| `embeddings[].path` |  | string | yes |  |  | Bobot embedding yang ditentukan melalui URL atau path file. |
| `embeddings[].tokens` |  | array of string \| null | no |  |  | Daftar token embedding, dengan penamaan otomatis jika dihilangkan: setiap vektor dalam file embedding menerima token dari urutan <s0>, <s1>, ... . |
| `enable_safety_checker` |  | boolean | no | `true` |  | Aktivasi pemeriksa keamanan; true mengaktifkan pemeriksaan. |
| `safety_checker_version` |  | enum (string) | no | `v1` | `v1`, `v2` | Pilih antara pemeriksa keamanan ViT khusus (`v2`) dan pemeriksa CompVis (`v1`, bawaan). |
| `expand_prompt` |  | boolean | no | `false` |  | Perluasan prompt dengan prompt tambahan, diaktifkan saat true. |
| `format` |  | enum (string) | no | `jpeg` | `jpeg`, `png` | Format file untuk gambar hasil. |
| `request_id` |  | string | no | `` |  | Pengenal permintaan untuk menghubungkan respons dengan permintaan yang menghasilkannya. |
| `preserve_aspect_ratio` |  | boolean | no | `false` |  | Atur ke true untuk mempertahankan rasio aspek gambar yang dihasilkan bahkan saat ukurannya melebihi batas. Untuk memperoleh proporsi yang benar melalui pemotongan setelah pembuatan, atur crop_output ke True. Nilai lebar atau tinggi yang tidak habis dibagi 32 tetap dibulatkan ke kelipatan 32 terdekat, dan pengubahan ukuran ini secara bawaan tidak mempertahankan rasio aspek. |
| `crop_output` |  | boolean | no | `false` |  | Memotong output yang dihasilkan ke rasio aspek yang benar saat bernilai true. |
| `openpose_image_url` |  | string (image URL) \| null | no |  |  | URL yang mengarah ke gambar kontrol. |
| `openpose_preprocess` |  | boolean | no | `true` |  | Mengontrol prapemrosesan gambar openpose. |
| `depth_image_url` |  | string (image URL) \| null | no |  |  | URL yang mengarah ke gambar kontrol. |
| `depth_preprocess` |  | boolean | no | `true` |  | Mengontrol prapemrosesan gambar depth. |
| `teed_image_url` |  | string (image URL) \| null | no |  |  | URL yang mengarah ke gambar kontrol. |
| `teed_preprocess` |  | boolean | no | `true` |  | Mengontrol prapemrosesan gambar teed. |
| `canny_image_url` |  | string (image URL) \| null | no |  |  | URL yang mengarah ke gambar kontrol. |
| `canny_preprocess` |  | boolean | no | `true` |  | Mengontrol prapemrosesan gambar canny. |
| `normal_image_url` |  | string (image URL) \| null | no |  |  | URL yang mengarah ke gambar kontrol. |
| `normal_preprocess` |  | boolean | no | `true` |  | Mengontrol prapemrosesan gambar normal. |
| `segmentation_image_url` |  | string (image URL) \| null | no |  |  | URL yang mengarah ke gambar kontrol. |
| `segmentation_preprocess` |  | boolean | no | `true` |  | Mengontrol prapemrosesan gambar segmentasi. |

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} | Informasi file untuk gambar yang dihasilkan. |
| `timings` | object |  |
| `seed` | integer | Seed pembuatan gambar, termasuk nilai yang dipilih secara acak jika input tidak menyertakan seed; jika tidak, nilai yang diberikan akan diulang. |
| `has_nsfw_concepts` | array of boolean | Menunjukkan apakah konsep NSFW muncul dalam gambar yang dihasilkan. |
| `prompt` | string | Prompt yang digunakan selama pembuatan gambar. |

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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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## 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