# Train Flux LoRAs For Portraits API

> 인물 크롭과 체크포인트 재개를 지원하는 인물 특화 FLUX LoRA 학습기

`images_data_url`의 이미지 zip에서 인물 중심 LoRA를 학습하며 최소 10개가 권장됩니다. 옵션으로 `trigger_phrase`, `learning_rate`(1e-6~0.001), `steps`(기본값 2500, 최대 10000), `subject_crop`, 다중 해상도 학습, `resume_from_checkpoint`가 있습니다. 가중치와 config를 반환합니다.

- Model id: `flux-lora-portrait-trainer`
- Made by: Black Forest Labs
- Category: Training
- Output: file
- Price per run: from $2.40 / run (cheapest run $2.40 (2400 API credits))
- Run: `POST https://api.doitong.com/v1/run/flux-lora-portrait-trainer`
- Quote: `POST https://api.doitong.com/v1/quote/flux-lora-portrait-trainer`
- Docs page: https://doitong.com/ko/developers/models/flux-lora-portrait-trainer

## Pricing

Price: **from $2.40 / 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 $6.00 = 6000 API credits (1 run × $6.00).

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/flux-lora-portrait-trainer` 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 |
|---|---|---|---|---|---|---|
| `images_data_url` |  | string | yes |  |  | 일관된 스타일의 이미지가 포함된 ZIP 아카이브 URL을 제공합니다. 선택적으로 이미지와 이름이 일치하는 캡션 텍스트 파일을 포함할 수 있습니다. 캡션 텍스트에는 `[trigger]`가 포함될 수 있으며, trigger_word를 지정하면 해당 자리 표시자를 값으로 바꿉니다. 최소 10개 이상의 이미지를 권장하며, 이미지 세트가 클수록 좋습니다. |
| `trigger_phrase` |  | string \| null | no |  |  | 기존 캡션의 `[trigger]`를 대체하거나 캡션이 없을 때 캡션으로 사용할 트리거 구문입니다(trigger_work). None은 트리거 단어를 비활성화합니다. |
| `learning_rate` |  | number | no | `9.0e-05` | 0.000001 – 0.001 | 학습 중 적용되는 학습률입니다. |
| `steps` |  | integer | no | `2500` | 1 – 10,000 | LoRA의 학습 단계 수입니다. |
| `multiresolution_training` |  | boolean | no | `true` |  | True로 설정하면 여러 해상도에서 학습할 수 있습니다. |
| `subject_crop` |  | boolean | no | `true` |  | True로 설정하면 이미지에서 피사체를 잘라낼 수 있습니다. |
| `data_archive_format` |  | string \| null | no |  |  | 아카이브 형식입니다. 생략하면 URL에서 추론합니다. |
| `resume_from_checkpoint` |  | string | no | `` |  | 학습을 재개하기 위한 체크포인트 URL입니다. |
| `create_masks` |  | boolean | no | `false` |  | True로 설정하면 피사체 마스크 생성을 활성화합니다. |

## Example input

Required parameters only:

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

A fuller example:

```json
{
  "images_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/flux-lora-portrait-trainer?wait=20" \
  -H "Authorization: Bearer $DOITONG_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "images_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/flux-lora-portrait-trainer?wait=20", {
  method: "POST",
  headers,
  body: JSON.stringify({
    "images_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 = {
    "images_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/flux-lora-portrait-trainer", 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": "flux-lora-portrait-trainer",
      "input": {
        "images_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": "flux-lora-portrait-trainer",
  "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": 6000,
    "usd": "6.00",
    "charged": 6000,
    "refunded": 0
  },
  "input": {
    "images_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} | 학습 구성 파일 URL입니다. |

## Related models

- [FLUX 2 \[klein\] 9b Base Trainer](https://doitong.com/developers/models/flux-2-klein-9b-base-trainer.md) (`flux-2-klein-9b-base-trainer`): $0.0043 / step
- [Flux 2 Klein 9B Base Trainer](https://doitong.com/developers/models/flux-2-klein-9b-base-trainer/edit.md) (`flux-2-klein-9b-base-trainer/edit`): $0.0139 / step
- [FLUX 2 Trainer](https://doitong.com/developers/models/flux-2-trainer.md) (`flux-2-trainer`): $0.0064 / step
- [FLUX 2 Trainer Edit](https://doitong.com/developers/models/flux-2-trainer/edit.md) (`flux-2-trainer/edit`): $0.0389 / step
- [FLUX 2 Trainer V2](https://doitong.com/developers/models/flux-2-trainer-v2.md) (`flux-2-trainer-v2`): $0.0064 / step
- [FLUX 2 Trainer V2 Edit](https://doitong.com/developers/models/flux-2-trainer-v2/edit.md) (`flux-2-trainer-v2/edit`): $0.0389 / step
- [Flux Kontext Trainer](https://doitong.com/developers/models/flux-kontext-trainer.md) (`flux-kontext-trainer`): from $1.25 / run
- [Train Flux LoRA](https://doitong.com/developers/models/flux-lora-fast-training.md) (`flux-lora-fast-training`): from $0.002 / run

## Links

- HTML page: https://doitong.com/developers/models/flux-lora-portrait-trainer
- 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=flux-lora-portrait-trainer
- Errors: https://doitong.com/developers/docs/errors
- Model details as JSON: https://api.doitong.com/v1/models/flux-lora-portrait-trainer