# Ideogram V4.0q LoRA Trainer API

> کیپشن والی تصاویر کی zip سے Ideogram V4 کے لیے حسبِ ضرورت LoRA ٹرین کرتا ہے

ذاتی نوعیت یا مخصوص انداز کے لیے اپنے کوائف مجموعے پر LoRA فٹ کرتا ہے۔ `images_data_url` تصاویر کے zip کی طرف اشارہ کرتا ہے، جن کے کیپشن اسی نام والی .txt فائل سے پڑھے جاتے ہیں، جبکہ `default_caption` غائب کیپشن پُر کرتا ہے۔ `steps` 100 سے 40000 تک ہے (ڈیفالٹ 1000)، اور `learning_rate` و `resolution` قابلِ ایڈجسٹ ہیں۔ LoRA وزن فائل اور ترتیب فائل واپس کرتا ہے۔

- 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/ur/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 |  |  | تربیتی ڈیٹا کی ZIP URL، جس میں تصاویر اور captions شامل ہیں؛ ہر caption الگ .txt فائل میں اسی تصویر کے filename stem کے ساتھ محفوظ ہوتا ہے۔ trainer اس target-only dataset سے کام کرتا ہے جسے prepare_dataset_dir تیار کرتا ہے۔ |
| `steps` |  | integer | no | `1000` | 100 – 40,000 | LoRA تربیت کے مراحل کی تعداد۔ |
| `default_caption` |  | string \| null | no |  |  | جب force_default_caption true ہو تو ہر تصویر پر لاگو ہونے والا caption، جو caption فائلوں کو نظرانداز کرتا ہے؛ بصورت دیگر یہ صرف ان تصاویر کے لیے استعمال ہوتا ہے جن کی caption فائل موجود نہ ہو۔ |
| `resolution` |  | string | no | `auto` |  | تربیتی تصویر کے ابعاد، بطور پری سیٹ، auto، یا کسٹم WIDTHxHEIGHT string متعین کیے جاتے ہیں، جس میں ہر بعد 16 سے قابل تقسیم ہو۔ مرکز کی کراپنگ تصاویر کو کبھی اپ اسکیل نہیں کرتی؛ auto ایسا سب سے بڑا کراپ منتخب کرتا ہے جو dataset میں مشترک ہو اور جسے بڑا کرنے کی ضرورت نہ ہو۔ پری سیٹس 1024x1024 (square)، 1536x1024 (landscape)، 1024x1536 (portrait)، 1920x1088 (widescreen)، 2048x768 (ultrawide)، 1024x1792 (phone_wallpaper)، اور 1584x400 (social_banner) ہیں۔ |
| `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} | تربیت سے تیار کردہ Ideogram V4 LoRA weights کا URL۔ |
| `config_file` | object {url, content_type, file_name, file_size} | عوامی طور پر دستیاب تربیتی کنفیگریشن کا 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