# TRELLIS.2 Trainer API

> Entrenescha adaptaders LoRA per Trellis 2 sin in zip d'assets 3D, per fin a trais denoisers

Fine-tunes adaptaders d'in zip a `data_url`, che cuntegna assets 3D cruts u in dataset preproces­sà. `denoisers` tscherna 1 to 3 finamiras, mintgina entrenada sco adaptader separà. Parameters: `resolution` 512 u 1024, `rank` 16, 32 u 64, `learning_rate` e `training_steps` da 100 to 10000. Returnescha ils adaptaders, eventuals problems e las datas preprocessadas reutilisablas.

- Model id: `trellis-2-lora-trainer`
- Made by: Microsoft
- Category: Training
- Output: file
- Price per run: from $1.7508 / run (cheapest run $1.751 (1751 API credits))
- Run: `POST https://api.doitong.com/v1/run/trellis-2-lora-trainer`
- Quote: `POST https://api.doitong.com/v1/quote/trellis-2-lora-trainer`
- Docs page: https://doitong.com/rm/developers/models/trellis-2-lora-trainer

## Pricing

Price: **from $1.7508 / 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 $15.973 = 15973 API credits (1 run × $15.9723).

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/trellis-2-lora-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 |
|---|---|---|---|---|---|---|
| `data_url` |  | string | yes |  |  | URL da l'archiv dal dataset: furnescha assets 3D cruds en ina .zip, u utilisescha la zip dal dataset TRELLIS.2 preelavurada returnada da data_preprocessing. |
| `denoisers` |  | array of enum (string) | no |  | 1 – 3 items | Selecziun da 1–3 denoisers TRELLIS.2 distincts da sparse_structure, geometry e texture per il fine-tuning. La trenaziun parallela utilisescha in dataset preelavurà commun per producir in adapter LoRA separat per mintga entrada selecziunada. |
| `resolution` |  | enum (integer) | no | `512` | `512`, `1024` | La resoluziun da trenaziun sto esser eguala a la resoluziun da preelavuraziun dal dataset. La trenaziun da geometria e textura ha configuraziuns separadas per 512 e 1024, entant che la trenaziun da la structura sparsa parta ina configuraziun per omaduas resoluziuns. |
| `rank` |  | enum (integer) | no | `32` | `16`, `32`, `64` | Rank da l'adapter LoRA; valurs pli grondas porschan ina capacitad pli gronda da l'adapter. |
| `learning_rate` |  | number | no | `0.0001` | 0.000001 – 0.01 | Rate d'emprender utilisà per optimar LoRA. |
| `training_steps` |  | integer | no | `1000` | 100 – 10,000 | Dumber total da pass da trenaziun. |

## Example input

Required parameters only:

```json
{
  "data_url": "https://example.com/your-file.zip"
}
```

A fuller example:

```json
{
  "data_url": "https://example.com/your-file.zip"
}
```

## 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/trellis-2-lora-trainer?wait=20" \
  -H "Authorization: Bearer $DOITONG_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "data_url": "https://example.com/your-file.zip"
  }'

# 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/trellis-2-lora-trainer?wait=20", {
  method: "POST",
  headers,
  body: JSON.stringify({
    "data_url": "https://example.com/your-file.zip"
  }),
});
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 = {
    "data_url": "https://example.com/your-file.zip",
}

# Run the model; wait up to 20 s for the finished result in the same call
r = requests.post(API + "/run/trellis-2-lora-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": "trellis-2-lora-trainer",
      "input": {
        "data_url": "https://example.com/your-file.zip"
      },
      "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": "trellis-2-lora-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": 15973,
    "usd": "15.973",
    "charged": 15973,
    "refunded": 0
  },
  "input": {
    "data_url": "https://example.com/your-file.zip"
  },
  "output": {
    "adapters": [
      {
        "denoiser": "sparse_structure",
        "resolution": 1,
        "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
        },
        "rank": 1,
        "training_steps": 1,
        "learning_rate": 1.0
      }
    ],
    "failed": [
      {
        "denoiser": "sparse_structure",
        "error": "string"
      }
    ],
    "preprocessed_data_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 |
|---|---|---|
| `adapters` | array of object {denoiser, resolution, lora_file, rank, training_steps, learning_rate} | Datotecas d'adapter LoRA colliadas cun lur champs d'inferenza trellis-2-lora correspundents. La resposta cuntegna in adapter per mintga denoiser la trenaziun dal qual è reussida; utilisescha sia datoteca en il champ correspundent. |
| `failed` | array of object {denoiser, error} | Resultats da trenaziun dals denoisers betg reussids; qua cumparan mo denoisers che n'han betg terminà la trenaziun, pia resta quest champ vid sch'els han tuts reussì. |
| `preprocessed_data_file` | object {url, content_type, file_name, file_size} | Zip dal dataset TRELLIS.2 preelavurà utilisà durant la trenaziun e disponibel per reutilisaziun. |

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## Links

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