# TRELLIS.2 Trainer API

> 3D ਐਸੈਟਾਂ ਦੀ zip ਉੱਤੇ Trellis 2 ਲਈ LoRA ਐਡੈਪਟਰਾਂ ਨੂੰ ਵੱਧ ਤੋਂ ਵੱਧ ਤਿੰਨ ਡੀਨੋਇਜ਼ਰਾਂ ਲਈ ਟ੍ਰੇਨ ਕਰਦਾ ਹੈ

`data_url` ਉੱਤੇ ਦਿੱਤੀ zip ਤੋਂ ਐਡੈਪਟਰਾਂ ਨੂੰ ਫਾਈਨ-ਟਿਊਨ ਕਰਦਾ ਹੈ, ਜਿਸ ਵਿੱਚ ਕੱਚੇ 3D ਐਸੈਟ ਜਾਂ ਪ੍ਰੀਪ੍ਰੋਸੈੱਸਡ ਡੇਟਾਸੈੱਟ ਹੁੰਦਾ ਹੈ। `denoisers` 1 ਤੋਂ 3 ਟਾਰਗੇਟ ਚੁਣਦਾ ਹੈ, ਜਿਨ੍ਹਾਂ ਵਿੱਚੋਂ ਹਰ ਇੱਕ ਨੂੰ ਆਪਣੇ ਐਡੈਪਟਰ ਵਜੋਂ ਟ੍ਰੇਨ ਕੀਤਾ ਜਾਂਦਾ ਹੈ। ਸੈਟਿੰਗਾਂ: `resolution` 512 ਜਾਂ 1024, `rank` 16, 32 ਜਾਂ 64, `learning_rate`, ਅਤੇ 100 ਤੋਂ 10000 ਤੱਕ `training_steps`। ਇਹ ਐਡੈਪਟਰ, ਕਿਸੇ ਵੀ ਅਸਫਲਤਾ ਅਤੇ ਦੁਬਾਰਾ ਵਰਤਣਯੋਗ ਪ੍ਰੀਪ੍ਰੋਸੈੱਸਡ ਡੇਟਾ ਵਾਪਸ ਕਰਦਾ ਹੈ।

- 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/pa/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 |  |  | ਡੇਟਾਸੈੱਟ ਆਰਕਾਈਵ URL: .zip ਵਿੱਚ ਪੈਕ ਕੀਤੇ raw 3D assets ਦਿਓ, ਜਾਂ data_preprocessing ਵੱਲੋਂ ਵਾਪਸ ਕੀਤਾ preprocessed TRELLIS.2 dataset zip ਵਰਤੋ। |
| `denoisers` |  | array of enum (string) | no |  | 1 – 3 items | fine-tuning ਲਈ sparse_structure, geometry, ਅਤੇ texture ਵਿੱਚੋਂ 1–3 ਵੱਖਰੇ TRELLIS.2 denoisers ਦੀ ਚੋਣ। Parallel training ਇੱਕ ਸਾਂਝੇ preprocessed dataset ਦੀ ਵਰਤੋਂ ਕਰਕੇ ਚੁਣੀ ਗਈ ਹਰ entry ਲਈ ਵੱਖਰਾ LoRA adapter ਤਿਆਰ ਕਰਦੀ ਹੈ। |
| `resolution` |  | enum (integer) | no | `512` | `512`, `1024` | Training ਰੈਜ਼ੋਲਿਊਸ਼ਨ dataset ਦੇ preprocessing ਰੈਜ਼ੋਲਿਊਸ਼ਨ ਦੇ ਬਰਾਬਰ ਹੋਣਾ ਚਾਹੀਦਾ ਹੈ। Geometry ਅਤੇ texture training ਵਿੱਚ 512 ਅਤੇ 1024 ਲਈ ਵੱਖਰੀਆਂ configurations ਹਨ, ਜਦਕਿ sparse-structure training ਇਨ੍ਹਾਂ ਰੈਜ਼ੋਲਿਊਸ਼ਨਾਂ ਲਈ ਇੱਕੋ configuration ਸਾਂਝੀ ਕਰਦੀ ਹੈ। |
| `rank` |  | enum (integer) | no | `32` | `16`, `32`, `64` | LoRA adapter ਦਾ rank; ਵੱਡੇ ਮੁੱਲ adapter ਦੀ ਵੱਧ ਸਮਰੱਥਾ ਦਿੰਦੇ ਹਨ। |
| `learning_rate` |  | number | no | `0.0001` | 0.000001 – 0.01 | LoRA ਨੂੰ optimize ਕਰਨ ਲਈ ਵਰਤੀ ਗਈ learning rate। |
| `training_steps` |  | integer | no | `1000` | 100 – 10,000 | training ਕਦਮਾਂ ਦੀ ਕੁੱਲ ਗਿਣਤੀ। |

## 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} | ਉਨ੍ਹਾਂ ਦੇ ਸੰਬੰਧਿਤ trellis-2-lora inference fields ਨਾਲ ਮੈਪ ਕੀਤੀਆਂ LoRA adapter ਫ਼ਾਈਲਾਂ। ਜਿਨ੍ਹਾਂ denoisers ਦੀ training ਸਫਲ ਰਹੀ, ਉਨ੍ਹਾਂ ਵਿੱਚੋਂ ਹਰ ਇੱਕ ਲਈ response ਵਿੱਚ ਇੱਕ adapter ਸ਼ਾਮਲ ਹੁੰਦਾ ਹੈ; ਉਸਦੀ ਫ਼ਾਈਲ ਮਿਲਦੇ field ਵਿੱਚ ਵਰਤੋ। |
| `failed` | array of object {denoiser, error} | ਅਸਫਲ denoiser training ਨਤੀਜੇ; ਇੱਥੇ ਸਿਰਫ਼ ਉਹ denoisers ਦਿਖਾਈ ਦਿੰਦੇ ਹਨ ਜਿਨ੍ਹਾਂ ਦੀ training ਪੂਰੀ ਨਹੀਂ ਹੋਈ, ਇਸ ਲਈ ਸਾਰੇ ਬੇਨਤੀ ਕੀਤੇ denoisers ਸਫਲ ਹੋਣ 'ਤੇ ਇਹ ਖਾਲੀ ਰਹਿੰਦਾ ਹੈ। |
| `preprocessed_data_file` | object {url, content_type, file_name, file_size} | training ਦੌਰਾਨ ਵਰਤੀ ਗਈ ਅਤੇ ਦੁਬਾਰਾ ਵਰਤੋਂ ਲਈ ਉਪਲਬਧ preprocessed TRELLIS.2 dataset zip। |

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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