# Krea 2 Trainer

> Train a custom LoRA on your own images to teach Krea 2 a new subject, character, or style. Provide a set of training images (and an optional trigger word), and the trainer outputs LoRA weights you can use for inference with the Krea 2 LoRA endpoint.


## Overview

- **Endpoint**: `https://fal.run/fal-ai/krea-2-trainer`
- **Model ID**: `fal-ai/krea-2-trainer`
- **Category**: training
- **Kind**: training
**Tags**: lora, personalization



## Pricing

Your request will cost **$0.0028 per step** (minimum of 100 steps is charged). For **$2.80** you can fine-tune a LoRA for **1000 steps**.

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 to a .zip archive of training images (PNG/JPG/JPEG/WebP). Each image may carry a same-stem caption: an image named ROOT.EXT (e.g. 001.jpg) pairs with a caption file named ROOT.txt (e.g. 001.txt). Images with no caption file fall back to `trigger_phrase`, so each image must have either a caption file or a non-empty `trigger_phrase` — otherwise the request is rejected (422).

- **`trigger_phrase`** (`string`, _optional_):
  Instance phrase used as the caption for any image that ships no .txt caption file (DreamBooth-style); also seeds the default validation prompts. Required when some images have no caption file. Default value: `""`
  - Default: `""`

- **`steps`** (`integer`, _optional_):
  Number of LoRA training steps. Default value: `1000`
  - Default: `1000`
  - Range: `100` to `10000`

- **`learning_rate`** (`float`, _optional_):
  AdamW learning rate. Krea recommends 3e-4–7e-4 (constant schedule). Default value: `0.0005`
  - Default: `0.0005`
  - Range: `0.000001` to `0.01`



**Required Parameters Example**:

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

**Full Example**:

```json
{
  "images_data_url": "",
  "steps": 1000,
  "learning_rate": 0.0005
}
```


### Output Schema

The API returns the following output format:

- **`lora_file`** (`File`, _required_):
  URL to the trained Krea-2 LoRA weights (safetensors).

- **`config_file`** (`File`, _required_):
  URL to the training/LoRA configuration JSON.



**Example Response**:

```json
{
  "lora_file": {
    "url": "",
    "content_type": "image/png",
    "file_name": "z9RV14K95DvU.png",
    "file_size": 4404019
  },
  "config_file": {
    "url": "",
    "content_type": "image/png",
    "file_name": "z9RV14K95DvU.png",
    "file_size": 4404019
  }
}
```


## Usage Examples

### cURL

```bash
curl --request POST \
  --url https://fal.run/fal-ai/krea-2-trainer \
  --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/krea-2-trainer",
    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/krea-2-trainer", {
  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/krea-2-trainer)
- [API Documentation](https://fal.ai/models/fal-ai/krea-2-trainer/api)
- [OpenAPI Schema](https://fal.ai/api/openapi/queue/openapi.json?endpoint_id=fal-ai/krea-2-trainer)

### 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)