# TRELLIS.2 Trainer API

> Melatih penyesuai LoRA untuk Trellis 2 daripada zip aset 3D, sehingga tiga penyahbising

Memperhalus penyesuai daripada zip di `data_url`, yang mengandungi aset 3D mentah atau set data yang dipraproses. `denoisers` memilih 1 hingga 3 sasaran, setiap satunya dilatih sebagai penyesuai tersendiri. Tetapan: `resolution` 512 atau 1024, `rank` 16, 32 atau 64, `learning_rate`, dan `training_steps` daripada 100 hingga 10000. Mengembalikan penyesuai, sebarang kegagalan dan data dipraproses yang boleh digunakan semula.

- 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/ms/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 arkib dataset: berikan aset 3D mentah dalam .zip, atau gunakan zip dataset TRELLIS.2 praproses yang dikembalikan oleh data_preprocessing. |
| `denoisers` |  | array of enum (string) | no |  | 1 – 3 items | Pemilihan 1–3 penyahhingar TRELLIS.2 berbeza daripada sparse_structure, geometry dan texture untuk penalaan halus. Latihan selari menggunakan dataset praproses bersama untuk menghasilkan penyesuai LoRA berasingan bagi setiap entri dipilih. |
| `resolution` |  | enum (integer) | no | `512` | `512`, `1024` | Resolusi latihan mesti sama dengan resolusi praproses dataset. Latihan geometry dan texture masing-masing mempunyai konfigurasi berasingan untuk 512 dan 1024, manakala latihan sparse-structure berkongsi konfigurasi merentas resolusi ini. |
| `rank` |  | enum (integer) | no | `32` | `16`, `32`, `64` | Pangkat penyesuai LoRA; nilai lebih besar memberikan kapasiti penyesuai lebih tinggi. |
| `learning_rate` |  | number | no | `0.0001` | 0.000001 – 0.01 | Kadar pembelajaran yang digunakan untuk mengoptimumkan LoRA. |
| `training_steps` |  | integer | no | `1000` | 100 – 10,000 | Jumlah bilangan langkah latihan. |

## 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} | Fail penyesuai LoRA dipetakan kepada medan inferens trellis-2-lora yang sepadan. Respons menyertakan satu penyesuai bagi setiap penyahhingar yang berjaya dilatih; gunakan failnya dalam medan sepadan. |
| `failed` | array of object {denoiser, error} | Hasil latihan penyahhingar yang tidak berjaya; hanya penyahhingar yang gagal menamatkan latihan muncul di sini, jadi kejayaan semua penyahhingar yang diminta menghasilkan nilai kosong. |
| `preprocessed_data_file` | object {url, content_type, file_name, file_size} | Zip dataset TRELLIS.2 praproses yang digunakan semasa latihan dan tersedia untuk digunakan semula. |

## Related models

- [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
- [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

## 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