# Z Image Turbo Trainer V2 API

> 캡션이 포함된 이미지 zip으로 Z Image Turbo용 사용자 지정 LoRA를 학습합니다

자체 데이터에 LoRA를 파인튜닝합니다. 이미지와 동일한 이름의 .txt 캡션 파일이 쌍으로 들어 있는 zip을 `image_data_url`로 제공합니다. `steps` 범위는 10~40000이며(기본값 2000), `learning_rate` 기본값은 0.0005이고, `default_caption`은 캡션이 없는 이미지에 사용됩니다. diffusers LoRA 가중치 파일과 config 파일을 출력합니다.

- Model id: `z-image-turbo-trainer-v2`
- Made by: Alibaba
- Category: Training
- Output: file
- Price per run: from $0.425 / run (cheapest run $0.425 (425 API credits))
- Run: `POST https://api.doitong.com/v1/run/z-image-turbo-trainer-v2`
- Quote: `POST https://api.doitong.com/v1/quote/z-image-turbo-trainer-v2`
- Docs page: https://doitong.com/ko/developers/models/z-image-turbo-trainer-v2

## Pricing

Price: **from $0.425 / run** (the lowest tier; the quote prices your exact input). Price per run: the input files are measured before the run (length, resolution, frame rate). The quote measures the same files and is exactly what the run charges.

The full example input below costs $1.70 = 1700 API credits (1 run × $1.70).

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/z-image-turbo-trainer-v2` 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

| Parameter | Canonical name | Type | Required | Default | Allowed values / range | Description |
|---|---|---|---|---|---|---|
| `image_data_url` |  | string | yes |  |  | 입력 이미지와 캡션 파일이 짝을 이루어 포함된 ZIP 아카이브의 URL입니다. 001.jpg와 같은 이미지 파일 이름에는 ROOT.EXT를 사용하고, 일치하는 캡션에는 001.txt와 같은 ROOT.txt를 사용합니다. 캡션 텍스트 파일이 없는 이미지에는 default_caption을 사용합니다. |
| `steps` |  | integer | no | `2000` | 10 – 40,000 | 전체 학습 단계 수입니다. |
| `default_caption` |  | string \| null | no |  |  | 누락된 캡션 파일 대신 사용할 캡션입니다. None으로 설정하면 누락을 오류로 처리합니다. |
| `learning_rate` |  | number | no | `0.0005` |  | 학습률입니다. |

## Example input

Required parameters only:

```json
{
  "image_data_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
}
```

A fuller example:

```json
{
  "image_data_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
}
```

## 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/z-image-turbo-trainer-v2?wait=20" \
  -H "Authorization: Bearer $DOITONG_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "image_data_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
  }'

# 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/z-image-turbo-trainer-v2?wait=20", {
  method: "POST",
  headers,
  body: JSON.stringify({
    "image_data_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
  }),
});
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_data_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
}

# Run the model; wait up to 20 s for the finished result in the same call
r = requests.post(API + "/run/z-image-turbo-trainer-v2", 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": "z-image-turbo-trainer-v2",
      "input": {
        "image_data_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
      },
      "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": "z-image-turbo-trainer-v2",
  "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": 1700,
    "usd": "1.70",
    "charged": 1700,
    "refunded": 0
  },
  "input": {
    "image_data_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
  },
  "output": {
    "diffusers_lora_file": {
      "url": "https://content.doitong.com/dev-api/2026/10/20/req_8k2m4q7x1z9c3v5b6n0p2r4t/0-output.jpeg",
      "content_type": "image/jpeg",
      "file_name": "0-output.jpeg",
      "file_size": 1843200
    },
    "config_file": {
      "url": "https://content.doitong.com/dev-api/2026/10/20/req_8k2m4q7x1z9c3v5b6n0p2r4t/0-output.jpeg",
      "content_type": "image/jpeg",
      "file_name": "0-output.jpeg",
      "file_size": 1843200
    }
  },
  "outputs": [
    {
      "type": "file",
      "url": "https://content.doitong.com/dev-api/2026/10/20/req_8k2m4q7x1z9c3v5b6n0p2r4t/0-output.jpeg",
      "content_type": "image/jpeg",
      "file_size": 1843200,
      "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 |
|---|---|---|
| `diffusers_lora_file` | object {url, content_type, file_name, file_size} | diffusers 형식의 학습된 LoRA 가중치를 가리키는 URL입니다. |
| `config_file` | object {url, content_type, file_name, file_size} | 학습된 모델의 구성 파일을 가리키는 URL입니다. |

## Related models

- [Qwen Image 2512 Trainer](https://doitong.com/developers/models/qwen-image-2512-trainer.md) (`qwen-image-2512-trainer`): from $0.75 / run
- [Qwen Image Edit 2509 Trainer](https://doitong.com/developers/models/qwen-image-edit-2509-trainer.md) (`qwen-image-edit-2509-trainer`): from $0.40 / run
- [Wan 2.2 14B Image Trainer](https://doitong.com/developers/models/wan-22-image-trainer.md) (`wan-22-image-trainer`): from $0.45 / run
- [Wan-2.1 LoRA Trainer · I2V 720p](https://doitong.com/developers/models/wan-trainer/i2v-720p.md) (`wan-trainer/i2v-720p`): $0.005 / step
- [Wan-2.1 LoRA Trainer · T2V](https://doitong.com/developers/models/wan-trainer/t2v.md) (`wan-trainer/t2v`): $0.005 / step
- [Wan-2.1 LoRA Trainer · T2V 14b](https://doitong.com/developers/models/wan-trainer/t2v-14b.md) (`wan-trainer/t2v-14b`): $0.005 / step
- [Wan-2.2 LoRA Trainer · I2V A14b](https://doitong.com/developers/models/wan-22-trainer/i2v-a14b.md) (`wan-22-trainer/i2v-a14b`): $0.005 / step
- [Wan-2.2 LoRA Trainer · T2V A14b](https://doitong.com/developers/models/wan-22-trainer/t2v-a14b.md) (`wan-22-trainer/t2v-a14b`): $0.004 / step

## Links

- HTML page: https://doitong.com/developers/models/z-image-turbo-trainer-v2
- 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=z-image-turbo-trainer-v2
- Errors: https://doitong.com/developers/docs/errors
- Model details as JSON: https://api.doitong.com/v1/models/z-image-turbo-trainer-v2