# Ideogram V4.0q LoRA Trainer API

> ਕੈਪਸ਼ਨ ਵਾਲੀਆਂ ਤਸਵੀਰਾਂ ਦੀ zip ਤੋਂ Ideogram V4 ਲਈ ਕਸਟਮ LoRA ਨੂੰ ਟ੍ਰੇਨ ਕਰਦਾ ਹੈ

ਵਿਅਕਤੀਗਤੀਕਰਨ ਜਾਂ ਕਿਸੇ ਖਾਸ ਸ਼ੈਲੀ ਲਈ ਤੁਹਾਡੇ ਆਪਣੇ ਡੇਟਾਸੈੱਟ ਉੱਤੇ LoRA ਫਿਟ ਕਰਦਾ ਹੈ। `images_data_url` ਤਸਵੀਰਾਂ ਦੀ zip ਵੱਲ ਇਸ਼ਾਰਾ ਕਰਦਾ ਹੈ, ਜਿਸ ਦੇ ਕੈਪਸ਼ਨ ਇੱਕੋ ਨਾਮ ਵਾਲੀਆਂ .txt ਫਾਈਲਾਂ ਤੋਂ ਪੜ੍ਹੇ ਜਾਂਦੇ ਹਨ, ਅਤੇ `default_caption` ਗੁੰਮ ਕੈਪਸ਼ਨਾਂ ਨੂੰ ਭਰਦਾ ਹੈ। `steps` 100 ਤੋਂ 40000 ਤੱਕ ਹੁੰਦਾ ਹੈ (ਡਿਫ਼ਾਲਟ 1000), ਅਤੇ `learning_rate` ਅਤੇ `resolution` ਬਦਲੇ ਜਾ ਸਕਦੇ ਹਨ। ਇਹ LoRA ਵਜ਼ਨ ਫਾਈਲ ਅਤੇ ਇੱਕ ਕੌਂਫਿਗ ਫਾਈਲ ਵਾਪਸ ਕਰਦਾ ਹੈ।

- Model id: `ideogram/v4/trainer`
- Made by: Ideogram
- Category: Training
- Output: file
- Price: $0.00675 / step (cheapest run $6.75 (6750 API credits))
- Run: `POST https://api.doitong.com/v1/run/ideogram/v4/trainer`
- Quote: `POST https://api.doitong.com/v1/quote/ideogram/v4/trainer`
- Docs page: https://doitong.com/pa/developers/models/ideogram/v4/trainer

## Pricing

Price: **$0.00675 / step**. Billed per training step (`steps`).

The full example input below costs $6.75 = 6750 API credits (1000 steps × 6.75 API credits).

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/ideogram/v4/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 |
|---|---|---|---|---|---|---|
| `images_data_url` |  | string | yes |  |  | ਚਿੱਤਰਾਂ ਅਤੇ captions ਵਾਲਾ training-data ZIP URL, ਜਿਸ ਵਿੱਚ ਹਰ caption ਆਪਣੀ ਚਿੱਤਰ ਦੀ filename stem ਵਾਲੀ .txt ਫ਼ਾਈਲ ਵਿੱਚ ਸਟੋਰ ਹੁੰਦਾ ਹੈ। Trainer ਉਸ target-only dataset ਤੋਂ ਕੰਮ ਕਰਦਾ ਹੈ ਜਿਸ ਨੂੰ prepare_dataset_dir ਤਿਆਰ ਕਰਦਾ ਹੈ। |
| `steps` |  | integer | no | `1000` | 100 – 40,000 | LoRA training ਲਈ steps ਦੀ ਗਿਣਤੀ। |
| `default_caption` |  | string \| null | no |  |  | ਜਦੋਂ force_default_caption true ਹੋਵੇ ਤਾਂ ਹਰ ਚਿੱਤਰ 'ਤੇ ਲਾਗੂ ਹੋਣ ਵਾਲਾ caption, ਜੋ caption ਫ਼ਾਈਲਾਂ ਨੂੰ ਓਵਰਰਾਈਡ ਕਰਦਾ ਹੈ; ਨਹੀਂ ਤਾਂ ਇਹ ਸਿਰਫ਼ ਉਹਨਾਂ ਚਿੱਤਰਾਂ ਲਈ ਵਰਤਿਆ ਜਾਂਦਾ ਹੈ ਜਿਨ੍ਹਾਂ ਦੀ caption ਫ਼ਾਈਲ ਨਹੀਂ ਹੈ। |
| `resolution` |  | string | no | `auto` |  | Training ਚਿੱਤਰ ਦੇ ਮਾਪ, ਜੋ preset, auto ਜਾਂ ਕਸਟਮ WIDTHxHEIGHT string ਵਜੋਂ ਦਿੱਤੇ ਜਾਂਦੇ ਹਨ ਅਤੇ ਹਰ ਮਾਪ 16 ਨਾਲ ਵੰਡਣਯੋਗ ਹੋਣਾ ਚਾਹੀਦਾ ਹੈ। Center cropping ਚਿੱਤਰਾਂ ਨੂੰ ਕਦੇ upscaling ਨਹੀਂ ਕਰਦੀ; auto dataset ਵਿੱਚ ਸਾਂਝਾ ਸਭ ਤੋਂ ਵੱਡਾ crop ਚੁਣਦਾ ਹੈ ਜਿਸ ਨੂੰ ਵੱਡਾ ਕਰਨ ਦੀ ਲੋੜ ਨਾ ਹੋਵੇ। ਪ੍ਰੀਸੈਟ ਹਨ 1024x1024 (square), 1536x1024 (landscape), 1024x1536 (portrait), 1920x1088 (widescreen), 2048x768 (ultrawide), 1024x1792 (phone_wallpaper) ਅਤੇ 1584x400 (social_banner)। |
| `learning_rate` |  | number | no | `0.0001` | 0.000001 – 0.01 |  |

## Example input

Required parameters only:

```json
{
  "images_data_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
}
```

A fuller example:

```json
{
  "images_data_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
}
```

## 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/ideogram/v4/trainer?wait=20" \
  -H "Authorization: Bearer $DOITONG_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "images_data_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
  }'

# 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/ideogram/v4/trainer?wait=20", {
  method: "POST",
  headers,
  body: JSON.stringify({
    "images_data_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
  }),
});
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 = {
    "images_data_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
}

# Run the model; wait up to 20 s for the finished result in the same call
r = requests.post(API + "/run/ideogram/v4/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": "ideogram/v4/trainer",
      "input": {
        "images_data_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
      },
      "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": "ideogram/v4/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": 6750,
    "usd": "6.75",
    "charged": 6750,
    "refunded": 0
  },
  "input": {
    "images_data_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
  },
  "output": {
    "diffusers_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
    },
    "config_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 |
|---|---|---|
| `diffusers_lora_file` | object {url, content_type, file_name, file_size} | Training ਨਾਲ ਤਿਆਰ ਕੀਤੇ Ideogram V4 LoRA weights ਦਾ URL। |
| `config_file` | object {url, content_type, file_name, file_size} | ਜਨਤਕ ਤੌਰ 'ਤੇ ਉਪਲਬਧ training configuration ਦਾ URL। |

## Related models

- [Ideogram V4.0 Text to Image](https://doitong.com/developers/models/ideogram/v4.md) (`ideogram/v4`): from $0.0075 / megapixel of output image
- [Ideogram V4.0q \[fast\]](https://doitong.com/developers/models/ideogram/v4/fast.md) (`ideogram/v4/fast`): from $0.00525 / megapixel of output image
- [Ideogram V4.0q \[instant\]](https://doitong.com/developers/models/ideogram/v4/instant.md) (`ideogram/v4/instant`): $0.0075 / megapixel of output image
- [Ideogram V4.0q Text to Image (LoRA)](https://doitong.com/developers/models/ideogram/v4/lora.md) (`ideogram/v4/lora`): from $0.0113 / megapixel of output image
- [Ideogram V4.0q Image to Image](https://doitong.com/developers/models/ideogram/v4/image-to-image.md) (`ideogram/v4/image-to-image`): from $0.0075 / megapixel of output image
- [Ideogram V4.0q Image to Image LoRA](https://doitong.com/developers/models/ideogram/v4/image-to-image/lora.md) (`ideogram/v4/image-to-image/lora`): from $0.0113 / megapixel of output image
- [Ideogram V4.0q Tiling](https://doitong.com/developers/models/ideogram/v4/tiling.md) (`ideogram/v4/tiling`): from $0.03 / megapixel
- [Ideogram V4.0q Tiling LoRA](https://doitong.com/developers/models/ideogram/v4/tiling/lora.md) (`ideogram/v4/tiling/lora`): from $0.0338 / megapixel

## Links

- HTML page: https://doitong.com/developers/models/ideogram/v4/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=ideogram/v4/trainer
- Errors: https://doitong.com/developers/docs/errors
- Model details as JSON: https://api.doitong.com/v1/models/ideogram/v4/trainer