# Krea 2 Trainer API

> Iħarreġ LoRA personalizzat ta’ Krea 2 minn zip ta’ immaġnijiet, b’auto-captioning fakultattiv

Iwettaq irfinar ta’ LoRA fuq .zip ta’ immaġnijiet f’`images_data_url`, b’deskrizzjonijiet .txt fakultattivi bl-istess isem u `trigger_phrase`. Il-modi ta’ `auto_captioning` huma Off, Object/Character, Style u Custom. `steps` huwa minn 50 sa 10000 (100 prestabbilit), u `resolution` hija 768 jew 1024. Joħroġ il-piżijiet ta’ LoRA u fajl ta’ konfigurazzjoni.

- 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/mt/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 għal .zip ta' immaġnijiet ta' taħriġ li fih fajls PNG/JPG/JPEG/WebP; talba tfalli b'422 jekk kwalunkwe immaġni ma jkollhiex la fajl tal-caption u lanqas `trigger_phrase` mhux vojt. Il-captions huma fakultattivi meta `trigger_phrase` jipprovdi l-fallback, u inkella uża l-bażijiet tal-ismijiet tal-fajls korrispondenti: ROOT.EXT jingħaqad ma' ROOT.txt, pereżempju 001.jpg ma' 001.txt. |
| `trigger_phrase` |  | string | no | `` |  | Frażi ta' istanza bl-istil ta' DreamBooth meħtieġa jekk `auto_captioning` ikun Off u kwalunkwe immaġni ma jkollhiex fajl tal-caption. Tipprovdi captions fejn il-fajls .txt huma nieqsa u tipprovdi t-test tal-bidu għall-prompts ta' validazzjoni prestabbiliti. |
| `auto_captioning` |  | enum (string) | no | `Off` | `Off`, `Object/Character`, `Style`, `Custom` | Modalità fakultattiva għall-ittikkettar: Object/Character, Style, u Custom jinvokaw dataset-captioner b’google/gemma-4-31B-it, filwaqt li Off iżomm ir-rekwiżit eżistenti li kull immaġni jkollha caption .txt b’stemma li taqbel jew tuża trigger_phrase bħala alternattiva. Għall-captions iġġenerati, trigger_phrase jidħol fil-prompt tal-ittikkettar u jiġi normalizzat fis-sidecars li jirriżultaw skont il-konvenzjoni magħżula għall-ittikkettar tal-LoRA. |
| `steps` |  | integer | no | `100` | 50 – 10,000 | Għadd ta' passi għat-taħriġ LoRA. |
| `learning_rate` |  | number | no | `0.0005` | 0.000001 – 0.01 | Rata tat-tagħlim tal-fattur B tal-LoRA, applikata wkoll għal A jekk `lora_a_lr_multiplier=1`; taħt muon, tirrappreżenta r-rata ekwivalenti għal AdamW skalata b’match_rms_adamw. Għal skeda kostanti, Krea tirrakkomanda l-medda 3e-4–7e-4. |
| `resolution` |  | enum (integer) | no | `768` | `768`, `1024` | Riżoluzzjoni tat-taħriġ, fejn 1024 tagħti prijorità lil dettall fin massimu u 768 toffri taħriġ madwar 1.7x aktar mgħaġġel, użu mnaqqas tal-VRAM, u kwalità tal-istil kważi identika. L-immaġnijiet jiġu skalati biex ikopru l-kwadru fil-mira, imbagħad jinqatgħu miċ-ċentru. |
| `debug_dataset` |  | boolean | no | `false` |  | Għażla għat-tniżżil tad-dataset li turi eżattament dak li ntuża fit-taħriġ, ippakkjat bħala arkivju wara l-mili tal-captions jew il-ġenerazzjoni awtomatika tal-captions. |

## 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 tat-tniżżil tal-piżijiet LoRA imħarrġa ta’ Krea-2 fil-format safetensors. |
| `config_file` | object {url, content_type, file_name, file_size} | Link għal fajl JSON li fih il-konfigurazzjoni tat-taħriġ/LoRA. |
| `prompt_enhancement_system_prompt` | string \| null | Prompt tas-sistema rritornat b’`auto_captioning` attivat, biex jgħin jikteb jew jirfina prompts għall-inference bl-istess konvenzjoni tal-captions bħad-data tat-taħriġ ittikkettata awtomatikament. |
| `debug_dataset` | object {url, content_type, file_name, file_size} \| null | Tniżżil tad-data tat-taħriġ ipprovdut meta `debug_dataset` ikun attivat: arkivju li fih id-dataset ippreparat u l-captions iġġenerati awtomatikament. |

## 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