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FLUX.1 LoRA Fast Training

Train custom FLUX LoRA models for styles, people and other subjects at blazing speeds. From concept to model in minutes, not hours.

Overview

FLUX.1 LoRA Fast Training enables developers to train custom LoRA (Low-Rank Adaptation) models on top of the FLUX.1 base model. This training service allows you to create personalized models that can generate images in specific styles, featuring particular people, objects, or artistic approaches.

Key Capabilities

Train custom LoRA models with unprecedented speed and control:

• Train styles, people and other subjects at blazing speeds
• Fast training in significantly reduced time compared to traditional methods
• Support for style training and character/object training
• Optimized for both individual creators and production environments
• Commercial usage rights included for trained models

Popular Use Cases

Style Training Create custom artistic styles by training on a dataset of consistent artwork. Perfect for artists wanting to replicate specific visual aesthetics or art movements.

Character Training Train models to generate specific people or characters consistently across different scenarios and contexts.

Product Training Create LoRA models for specific products or brand elements to maintain visual consistency across marketing materials.

Creative Projects Develop unique visual styles for games, animations, or other creative projects requiring consistent artistic direction.

Getting Started

Getting up and running with FLUX LoRA Training is straightforward. First, install your preferred SDK:

For JavaScript/TypeScript:

npm install --save @fal-ai/client

For Python:

pip install fal-client

Then initialize with your API key:

import { fal } from '@fal-ai/client'

fal.config({
  credentials: 'YOUR_FAL_KEY'
})

const result = await fal.subscribe('fal-ai/flux-lora-fast-training', {
  input: {
    images_data_url: 'YOUR_TRAINING_IMAGES_ZIP_URL',
    trigger_word: 'unique_token_name',
    is_style: true
  }
})

Training Parameters

The training service accepts several parameters to configure your model:

{
  images_data_url: string,    // ZIP file containing training images
  trigger_word: string,       // Unique word to activate your LoRA
  is_style?: boolean,         // Set to true for style training
  steps?: number,             // Number of training steps (default: 1000)
  // Additional configuration options available
}

Best Practices

For optimal training results:

Dataset Preparation Use high-resolution images (ideally 1024x1024 or greater) with no compression artifacts. Ensure consistent quality and style across your training dataset.

Trigger Words Choose unique trigger words like "txcl" combined with descriptive terms (e.g., "txcl painting") to ensure your LoRA can be activated reliably.

Training Steps Start with the default 1000 steps. Adjust based on results - too few steps may not capture the style, while too many can lead to overfitting.

Style vs Character Training Use the is_style: true parameter for style training to disable auto-captioning and segmentation masks that aren't needed for artistic style transfer.

Technical Specifications

Training Process

• Optimized training pipeline for fast convergence
• Supports various image formats for training data
• Automatic preprocessing and optimization
• Default training: 1000 steps (configurable)

Output

• Trained LoRA model files
• Compatible with FLUX.1 inference endpoints
• Can be used with fal.ai/models/fal-ai/flux-lora for generation

Pricing and Usage

Your request will cost $2 per training run (scales linearly with steps). This transparent pricing makes it cost-effective for experimentation and production use:

• Fixed $2 base cost per training run
• Scales linearly with the number of training steps
• No hidden fees or minimum commitments
• Commercial usage rights included

Using Your Trained LoRA

Once training is complete, you can use your custom LoRA with the FLUX.1 generation endpoints:

// Generate images using your trained LoRA
const result = await fal.subscribe('fal-ai/flux-lora', {
  input: {
    prompt: 'A beautiful landscape in the style of txcl painting',
    loras: [{ path: 'YOUR_TRAINED_LORA_URL', scale: 1.0 }]
  }
})

Support and Resources

Comprehensive support ensures your training success:

• Detailed training guides and best practices
• Community examples and showcases
• Technical documentation at docs.fal.ai
• Active Discord community for tips and support

Ready to train your custom FLUX LoRA model? Get started now and create personalized AI models in minutes.

Get Started Now | View Documentation | Join Discord