# Qwen Image Trainer V2

> Qwen Image LoRA training


## Overview

- **Endpoint**: `https://fal.run/fal-ai/qwen-image-trainer-v2`
- **Model ID**: `fal-ai/qwen-image-trainer-v2`
- **Category**: training
- **Kind**: training
**Tags**: lora, personalization



## Pricing

Your request will cost **$0.002 per step** (minimum of 500 steps is charged). For **$2.0** 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:


- **`image_data_url`** (`string`, _required_):
  URL to the input data zip archive for text-to-image training.
  
  The zip should contain images with their corresponding text captions:
  
  image.EXT and image.txt
  For example:
  photo.jpg and photo.txt
  
  The text file contains the caption/prompt describing the target image.
  
  If no text file is provided for an image, the default_caption will be used.
  
  If no default_caption is provided and a text file is missing, the training will fail.

- **`learning_rate`** (`float`, _optional_):
  Learning rate for LoRA parameters. Default value: `0.0005`
  - Default: `0.0005`

- **`steps`** (`integer`, _optional_):
  Number of steps to train for Default value: `1000`
  - Default: `1000`
  - Range: `100` to `30000`, step: `100`

- **`default_caption`** (`string`, _optional_):
  Default caption to use when caption files are missing. If None, missing captions will cause an error.



**Required Parameters Example**:

```json
{
  "image_data_url": ""
}
```

**Full Example**:

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


### Output Schema

The API returns the following output format:

- **`diffusers_lora_file`** (`File`, _required_):
  URL to the trained diffusers lora weights.

- **`config_file`** (`File`, _required_):
  URL to the configuration file for the trained model.



**Example Response**:

```json
{
  "diffusers_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/qwen-image-trainer-v2 \
  --header "Authorization: Key $FAL_KEY" \
  --header "Content-Type: application/json" \
  --data '{
     "image_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/qwen-image-trainer-v2",
    arguments={
        "image_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/qwen-image-trainer-v2", {
  input: {
    image_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/qwen-image-trainer-v2)
- [API Documentation](https://fal.ai/models/fal-ai/qwen-image-trainer-v2/api)
- [OpenAPI Schema](https://fal.ai/api/openapi/queue/openapi.json?endpoint_id=fal-ai/qwen-image-trainer-v2)

### 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)