# TRELLIS.2 Trainer API

> ᡝᠮᡠ ᡤᡳᠯᡤᠠᠨ 3D ᠠᠰᡳᠨᡳ zip ᠠᠴᠠ Trellis 2 ᡩᡝ LoRA ᠠᠯᡳᠨ ᡠᠯᠠᡥᠠᠮᡝ᠂ ᡠᠮᡝ ᡳᠯᠠᠨ ᡩᡝᠯᡤᡳᠨᡳ ᠵᠠᠰᡳᠮᡝᠨ ᡩᡝ

`data_url` ᡩᡝ ᡤᠠᠯᠠᠮᡝ ᠪᠠᠶᡳᡥᠠ zip ᠠᠴᠠ ᠠᠯᡳᠨ ᡩᠠᠯᡳᠨ ᡠᠯᠠᡥᠠᠮᡝ᠂ ᡤᡳᠯᡤᠠᠨ 3D ᠠᠰᡳᠨ ᠶᠠᠪᡠᠨ ᠵᠠᠰᡳᠮᡝᠨ ᡩᠠᠯᡳᠨ ᠪᠠᠶᡳᠮᠪᡳ᠉ `denoisers` 1 ᠠᠴᠠ 3 ᠮᡠᠶᠠᠯᠠᠨ ᠰᠣᠩᡤᠣᠯᠣᠮᡝ᠂ ᡝᠮᡠ ᡩᡝ ᡝᠮᡠ ᠠᠯᡳᠨ ᠪᠠᠶᡳᠮᠪᡳ᠉ ᠵᠠᠰᡳᠮᡝᠨ᠄ `resolution` 512 ᠶᠠᠪᡠᠨ 1024᠂ `rank` 16᠂ 32 ᠶᠠᠪᡠᠨ 64᠂ `learning_rate`᠂ ᠶᠠᠪᡠᠨ `training_steps` 100 ᠠᠴᠠ 10000 ᡩᡝ᠉ ᠠᠯᡳᠨ᠂ ᠪᠠᠶᠠᠨ ᠠᡴᡡ ᠶᠠᠪᡠᠨ ᠳᠠᠬᡳ ᠠᠰᡳᠯᠠᠮᡝ ᠪᠠᠶᡳᠮᠪᡳ᠉

- 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.ru/v1/run/trellis-2-lora-trainer`
- Quote: `POST https://api.doitong.ru/v1/quote/trellis-2-lora-trainer`
- Docs page: https://doitong.com/mnc/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.ru/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 |  |  | ᡩᠠᠯᠠᠮᡝ ᡳ ᠪᠠᠷᠠᠨ ᡳ URL᠉ raw 3D ᡝᠮᡤᡳ ᠪᡝ .zip ᡩᡝ ᠪᠠᠨᡩᡠᡥᠠᠨ ᡩᠠᠯᠠᠮᡝᠨᡝᠮᠪᡳ᠂ ᡝᠮᡝᠯᡝ TRELLIS.2 ᡩᠠᠯᠠᠮᡝ ᡳ zip ᠪᡝ data_preprocessing ᡩᡝ ᠪᡠᠶᡝᠮᡝᠨᡝᠮᠪᡳ᠉ |
| `denoisers` |  | array of enum (string) | no |  | 1 – 3 items | sparse_structure᠂ geometry᠂ texture ᡩᡝᡵᡳ 1–3 ᡝᠮᡠᠨ ᡩᠠᠯᠠᠮᡝᠨᡝᠮᠪᡳ ᡳ TRELLIS.2 ᠵᡝᠰᡝᠨ ᡳ ᠰᡝᠯᡝᠮᠪᡳ᠉ |
| `resolution` |  | enum (integer) | no | `512` | `512`, `1024` | ᠵᡝᠰᡝᠨ ᡳ ᠵᠠᠯᠠᠨ ᠪᡝ ᡩᠠᠯᠠᠮᡝ ᡳ ᠵᠠᠯᠠᠨ ᡩᡝ ᡝᠮᡠ ᠪᡳᠮᡝ ᡳᠨᡠ᠉ |
| `rank` |  | enum (integer) | no | `32` | `16`, `32`, `64` | LoRA ᡳ ᠵᠠᠯᠠᠨ ᡳ rank᠉ ᠠᠮᠪᠠ ᠪᡳᠮᡝ capacity ᠵᡝᠯᡥᡝᠮᠪᡳ᠉ |
| `learning_rate` |  | number | no | `0.0001` | 0.000001 – 0.01 | LoRA ᡳ ᠵᠠᠯᠠᠮᠪᡝ ᡳ learning rate᠉ |
| `training_steps` |  | integer | no | `1000` | 100 – 10,000 | ᠵᡝᠰᡝᠨ ᡳ ᡤᡝᠮᡠ ᠵᡝᠯᡥᡝᠨ᠉ |

## 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.ru/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.ru/v1/requests/REQUEST_ID?wait=20" \
  -H "Authorization: Bearer $DOITONG_API_KEY"
```

### JavaScript

```javascript
const API = "https://api.doitong.ru/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.ru/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.ru/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.ru/v1/requests/req_8k2m4q7x1z9c3v5b6n0p2r4t",
    "cancel": "https://api.doitong.ru/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 ᡳ ᡶᡳᠯᡝᠰᡝ ᡳ trellis-2-lora ᡳ ᠵᠠᠯᠠᠮᠪᡳ ᡳ ᡶᡳᠯᡩ ᡩᡝ ᡩᠠᠯᠠᠮᠪᡳ᠉ |
| `failed` | array of object {denoiser, error} | ᠠᠮᡤᠠᠯᠠᠮᡝ ᡳᠯᡥᡝᠨᡝᠮᠪᡳ ᡳ denoiser ᡳ ᠵᡝᠰᡝᠨ ᡳ ᡩᡠᠯᡝᠨ᠉ |
| `preprocessed_data_file` | object {url, content_type, file_name, file_size} | ᡝᡵᡝᠮᡝ ᠵᡝᠰᡝᠨᡳ TRELLIS.2 ᡳ zip ᠪᡝ ᠵᡝᠰᡝᠨ ᡩᡝ ᡠᠯᠠᠮᡝᠨᡝᠮᠪᡳ᠉ |

## 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.ru/v1/models/trellis-2-lora-trainer