# Flux Lora Trainer

> Fast-train your custom models with optimized pipelines, supporting various image formats, and requiring minimal 16GB VRAM for efficient fine-tuning.

## Overview

- **Model ID**: `flux_lora_trainer`
- **Category**: training
- **Provider**: modelslab
- **Status**: active
- **Screenshot**: `https://assets.modelslab.com/generations/7652d0aa-4790-481a-89af-febe06c86a83.webp`

## API Information

This model can be used via our HTTP API. See the API documentation and usage examples below.

### Endpoint

- **URL**: `https://modelslab.com/api/v6/trainer/train`
- **Method**: POST

### Parameters

- **`images`** (required): Upload or paste the URL to training images
  - Type: array

- **`trigger_word`** (required): Trigger word for the LoRA model
  - Type: text

- **`hf_username`** (optional): username for huggingface (Required)
  - Type: text

- **`hf_token`** (optional): hf token for the user (Required)
  - Type: text

- **`training_steps`** (optional): Recommended training steps are based on your dataset size. Training Steps = 100 to 150 × number of images. Example 10 images × 150 = 1500 steps. General Rule of Thumb: Fewer images = more steps per image. More images = fewer steps per image
  - Type: number (range: 1-10000)

- **`server_name`** (required): Select server to train your lora model
  - Type: select (options: NVIDIA H100 SXM ($3.00/hr))

## Usage Examples

### cURL

```bash
curl --request POST \
  --url https://modelslab.com/api/v6/trainer/train \
  --header "Content-Type: application/json" \
  --data '{
    "key": "YOUR_API_KEY",
    "model_id": "flux_lora_trainer",
    "trigger_word": "Pink",
    "training_steps": "1000",
    "server_name": "NVIDIA H100 80GB HBM3"
  }'
```

### Python

```python
import requests

response = requests.post(
    "https://modelslab.com/api/v6/trainer/train",
    headers={
        "Content-Type": "application/json"
    },
    json={
        "key": "YOUR_API_KEY",
        "model_id": "flux_lora_trainer",
        "trigger_word": "Pink",
        "training_steps": "1000",
        "server_name": "NVIDIA H100 80GB HBM3"
    }
)

print(response.json())
```

### JavaScript

```javascript
fetch("https://modelslab.com/api/v6/trainer/train", {
  method: "POST",
  headers: {
    "Content-Type": "application/json"
  },
  body: JSON.stringify({
    "key": "YOUR_API_KEY",
    "model_id": "flux_lora_trainer",
    "trigger_word": "Pink",
    "training_steps": "1000",
    "server_name": "NVIDIA H100 80GB HBM3"
  })
})
.then(response => response.json())
.then(data => console.log(data));
```

## Links

- [Model Playground](https://modelslab.com/models/flux_lora_trainer/flux_lora_trainer)
- [API Documentation](https://docs.modelslab.com)
- [ModelsLab Platform](https://modelslab.com)