# Ideogram

> Train Ideogram on your photos, your style, your subject, your look, from a small set of reference images to images that feel consistently yours 


## Overview

- **Endpoint**: `https://fal.run/fal-ai/ideogram/custom-models`
- **Model ID**: `fal-ai/ideogram/custom-models`
- **Category**: training
- **Kind**: training
**Tags**: stylized, transform



## Pricing

Your request will cost **$40** per training run. For **$50** you can run this model with approximately **1** times.

For more details, see [fal.ai pricing](https://fal.ai/pricing).

## API Information

This model can be used via our HTTP API or more conveniently via our client libraries.
See the input and output schema below, as well as the usage examples.


### Input Schema

The API accepts the following input parameters:


- **`images_data_url`** (`string`, _required_):
  URL of a ZIP archive of training images. The archive must contain between 10 and 100 images (JPEG, PNG, or WebP). You may include caption sidecar files (``<stem>.txt``) to guide training -- captions are matched to images by filename stem.



**Required Parameters Example**:

```json
{
  "images_data_url": ""
}
```


### Output Schema

The API returns the following output format:

- **`model_id`** (`string`, _required_):
  The Ideogram model id. Pass this to /generate.

- **`custom_model_uri`** (`string`, _required_):
  The trained model's URI. ``/generate`` resolves this automatically from the ``model_id``, but it is surfaced here for callers that use the ``custom_model_uri`` on the public Ideogram v3 generate endpoint directly.



**Example Response**:

```json
{
  "model_id": "",
  "custom_model_uri": ""
}
```


## Usage Examples

### cURL

```bash
curl --request POST \
  --url https://fal.run/fal-ai/ideogram/custom-models \
  --header "Authorization: Key $FAL_KEY" \
  --header "Content-Type: application/json" \
  --data '{
     "images_data_url": ""
   }'
```

### Python

Ensure you have the Python client installed:

```bash
pip install fal-client
```

Then use the API client to make requests:

```python
import fal_client

def on_queue_update(update):
    if isinstance(update, fal_client.InProgress):
        for log in update.logs:
           print(log["message"])

result = fal_client.subscribe(
    "fal-ai/ideogram/custom-models",
    arguments={
        "images_data_url": ""
    },
    with_logs=True,
    on_queue_update=on_queue_update,
)
print(result)
```

### JavaScript

Ensure you have the JavaScript client installed:

```bash
npm install --save @fal-ai/client
```

Then use the API client to make requests:

```javascript
import { fal } from "@fal-ai/client";

const result = await fal.subscribe("fal-ai/ideogram/custom-models", {
  input: {
    images_data_url: ""
  },
  logs: true,
  onQueueUpdate: (update) => {
    if (update.status === "IN_PROGRESS") {
      update.logs.map((log) => log.message).forEach(console.log);
    }
  },
});
console.log(result.data);
console.log(result.requestId);
```


## Additional Resources

### Documentation

- [Model Playground](https://fal.ai/models/fal-ai/ideogram/custom-models)
- [API Documentation](https://fal.ai/models/fal-ai/ideogram/custom-models/api)
- [OpenAPI Schema](https://fal.ai/api/openapi/queue/openapi.json?endpoint_id=fal-ai/ideogram/custom-models)

### fal.ai Platform

- [Platform Documentation](https://docs.fal.ai)
- [Python Client](https://docs.fal.ai/clients/python)
- [JavaScript Client](https://docs.fal.ai/clients/javascript)