# Krea 2 Trainer API

> Treina um LoRA personalizado do Krea 2 a partir de um zip de imagens, com legendagem automática opcional

Aperfeiçoa um LoRA num .zip de imagens em `images_data_url`, com legendas .txt opcionais com o mesmo nome e uma `trigger_phrase`. Os modos de `auto_captioning` são Off, Object/Character, Style e Custom. `steps` varia entre 50 e 10000 (predefinição 100) e `resolution` é 768 ou 1024. Produz pesos LoRA e um ficheiro de configuração.

- Model id: `krea-2-trainer`
- Made by: Krea
- Category: Training
- Output: file
- Price per run: from $0.30 / run (cheapest run $0.30 (300 API credits))
- Run: `POST https://api.doitong.com/v1/run/krea-2-trainer`
- Quote: `POST https://api.doitong.com/v1/quote/krea-2-trainer`
- Docs page: https://doitong.com/pt-PT/developers/models/krea-2-trainer

## Pricing

Price: **from $0.30 / 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 $0.30 = 300 API credits (1 run × $0.30).

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/krea-2-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 |  |  | URL para um .zip de imagens de treino que contém ficheiros PNG/JPG/JPEG/WebP; um pedido falha com 422 se alguma imagem não tiver um ficheiro de legenda nem um `trigger_phrase` não vazio. As legendas são opcionais quando `trigger_phrase` fornece a alternativa e, caso contrário, usam os nomes-base correspondentes: ROOT.EXT corresponde a ROOT.txt, por exemplo 001.jpg corresponde a 001.txt. |
| `trigger_phrase` |  | string | no | `` |  | Frase de instância ao estilo DreamBooth obrigatória se `auto_captioning` estiver Off e alguma imagem não tiver um ficheiro de legenda. Fornece legendas quando faltam ficheiros .txt e fornece o texto inicial para os prompts de validação predefinidos. |
| `auto_captioning` |  | enum (string) | no | `Off` | `Off`, `Object/Character`, `Style`, `Custom` | Modo de legendagem opcional: Object/Character, Style e Custom invocam dataset-captioner com google/gemma-4-31B-it, enquanto Off mantém o requisito existente de cada imagem ter uma legenda .txt com o mesmo nome-base ou usar trigger_phrase como alternativa. Nas legendas geradas, trigger_phrase entra no prompt de legendagem e é normalizado nos ficheiros auxiliares resultantes de acordo com a convenção de legendagem LoRA selecionada. |
| `steps` |  | integer | no | `100` | 50 – 10,000 | Número de passos do treino LoRA. |
| `learning_rate` |  | number | no | `0.0005` | 0.000001 – 0.01 | Taxa de aprendizagem do fator B da LoRA, também aplicada a A quando `lora_a_lr_multiplier=1`; com muon, representa a taxa equivalente a AdamW escalada por match_rms_adamw. Para um agendamento constante, a Krea recomenda o intervalo 3e-4–7e-4. |
| `resolution` |  | enum (integer) | no | `768` | `768`, `1024` | Resolução de treino: 1024 favorece o máximo detalhe fino e 768 oferece um treino aproximadamente 1,7x mais rápido, menor utilização de VRAM e qualidade de estilo quase idêntica. As imagens são dimensionadas para cobrir o quadrado-alvo e depois recortadas ao centro. |
| `debug_dataset` |  | boolean | no | `false` |  | Opção de transferência do conjunto de dados que expõe exatamente o que foi usado no treino, empacotado como um arquivo após o preenchimento retroativo das legendas ou a geração automática de legendas. |

## 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/krea-2-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/krea-2-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/krea-2-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": "krea-2-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": "krea-2-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": 300,
    "usd": "0.30",
    "charged": 300,
    "refunded": 0
  },
  "input": {
    "images_data_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
  },
  "output": {
    "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
    },
    "prompt_enhancement_system_prompt": "string",
    "debug_dataset": {
      "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 |
|---|---|---|
| `lora_file` | object {url, content_type, file_name, file_size} | URL de transferência dos pesos LoRA Krea-2 treinados, no formato safetensors. |
| `config_file` | object {url, content_type, file_name, file_size} | Ligação para um ficheiro JSON que contém a configuração de treino/LoRA. |
| `prompt_enhancement_system_prompt` | string \| null | Prompt de sistema devolvido com `auto_captioning` ativado, para ajudar a escrever ou aperfeiçoar prompts de inferência usando a mesma convenção de legendagem dos dados de treino legendados automaticamente. |
| `debug_dataset` | object {url, content_type, file_name, file_size} \| null | Transferência dos dados de treino disponibilizada quando `debug_dataset` está ativado: um arquivo que contém o conjunto de dados preparado e as respetivas legendas geradas automaticamente. |

## Related models

- [Krea 2 Text to Image Turbo LoRA](https://doitong.com/developers/models/krea-2/turbo/lora.md) (`krea-2/turbo/lora`): $0.01 / megapixel
- [Krea 2 Turbo](https://doitong.com/developers/models/krea-2/turbo.md) (`krea-2/turbo`): $0.008 / megapixel
- [ERNIE-Image Trainer](https://doitong.com/developers/models/ernie-image-trainer.md) (`ernie-image-trainer`): $0.0012 / step
- [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

## Links

- HTML page: https://doitong.com/developers/models/krea-2-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=krea-2-trainer
- Errors: https://doitong.com/developers/docs/errors
- Model details as JSON: https://api.doitong.com/v1/models/krea-2-trainer