# TRELLIS.2 Trainer API

> 3D اثاثوں کی zip پر Trellis 2 کے لیے LoRA اڈاپٹر تربیت دیتا ہے، زیادہ سے زیادہ تین ڈینوائزرز کے لیے

`data_url` پر موجود zip سے اڈاپٹرز کی فائن ٹیوننگ کرتا ہے، جس میں خام 3D اثاثے یا پہلے سے پروسیس شدہ ڈیٹاسیٹ ہوتا ہے۔ `denoisers` میں 1 سے 3 اہداف منتخب کیے جاتے ہیں، اور ہر ایک کو اپنے اڈاپٹر کے طور پر تربیت دی جاتی ہے۔ سیٹنگز: `resolution` 512 یا 1024، `rank` 16، 32 یا 64، `learning_rate`، اور `training_steps` 100 سے 10000 تک۔ یہ اڈاپٹرز، ناکامیاں اور دوبارہ قابل استعمال پری پروسیس شدہ ڈیٹا واپس کرتا ہے۔

- 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/ur/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 |  |  | Dataset archive URL: خام 3D assets کو .zip میں پیکیج کر کے فراہم کریں، یا data_preprocessing سے واپس آنے والا پہلے سے تیار TRELLIS.2 dataset zip استعمال کریں۔ |
| `denoisers` |  | array of enum (string) | no |  | 1 – 3 items | fine-tuning کے لیے sparse_structure، geometry اور texture میں سے 1–3 الگ TRELLIS.2 denoisers کا انتخاب۔ Parallel training مشترکہ preprocessed dataset استعمال کر کے ہر منتخب entry کے لیے الگ LoRA adapter بناتی ہے۔ |
| `resolution` |  | enum (integer) | no | `512` | `512`, `1024` | Training ریزولوشن dataset کے preprocessing ریزولوشن کے برابر ہونا چاہیے۔ Geometry اور texture training میں 512 اور 1024 کے لیے الگ configurations ہیں، جبکہ sparse-structure training ان ریزولوشنز کے لیے مشترکہ configuration استعمال کرتی ہے۔ |
| `rank` |  | enum (integer) | no | `32` | `16`, `32`, `64` | LoRA adapter کا rank؛ بڑی قدریں adapter کی زیادہ capacity فراہم کرتی ہیں۔ |
| `learning_rate` |  | number | no | `0.0001` | 0.000001 – 0.01 | LoRA کو optimize کرنے کے لیے استعمال ہونے والی learning rate۔ |
| `training_steps` |  | integer | no | `1000` | 100 – 10,000 | Training مراحل کی کل تعداد۔ |

## 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} | LoRA adapter فائلیں، جو متعلقہ trellis-2-lora inference fields سے منسلک ہیں۔ جواب میں ہر اس denoiser کے لیے ایک adapter شامل ہوتا ہے جس کی training کامیاب ہوئی؛ اس فائل کو متعلقہ field میں استعمال کریں۔ |
| `failed` | array of object {denoiser, error} | ناکام denoiser training کے نتائج؛ یہاں صرف وہ denoisers ظاہر ہوتے ہیں جن کی training مکمل نہیں ہوئی، اس لیے تمام مطلوبہ denoisers کی کامیابی پر یہ خالی رہتا ہے۔ |
| `preprocessed_data_file` | object {url, content_type, file_name, file_size} | Training کے دوران استعمال ہونے والا اور دوبارہ استعمال کے لیے دستیاب 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