Ultimate Guide to LoRA Training for Beginners (2025)

LoRA training lets you customize AI image generation for specific styles, characters, or concepts. While it sounds technical, anyone can train a LoRA with the right guidance. This guide takes you from understanding the basics to training your first successful model.

Quick Answer: LoRA (Low-Rank Adaptation) training teaches an existing AI model new concepts like faces, styles, or objects. You need 10-50 high-quality training images, a training tool like kohya_ss, and 1-4 hours of training time. Results can be excellent even for beginners, producing consistent character or style generation.

What is LoRA and Why Train One?

LoRA Explained Simply

LoRA stands for Low-Rank Adaptation. Instead of retraining an entire AI model (expensive, slow), LoRA trains a small adapter that modifies the base model's behavior.

Analogy: Think of it like learning a new accent. You don't forget English, you just modify how you speak it. LoRA teaches the AI model a new "accent" for generating specific content.

Benefits of Training LoRAs

Consistency: Generate the same character or style repeatedly Control: Precise concepts the base model doesn't understand Efficiency: Small files (10-200MB vs 2-7GB for full models) Stackable: Combine multiple LoRAs for complex effects Shareable: Easy to distribute and use

Common Use Cases

• Character LoRAs: Train on a specific person or character
• Style LoRAs: Capture artistic styles or aesthetics
• Concept LoRAs: Objects, clothing, settings
• Pose LoRAs: Specific body positions or compositions

For foundational understanding, see our What is LoRA Training guide.

Prerequisites

Hardware Requirements

Minimum:

• GPU: 8GB VRAM (limited to smaller batches)
• RAM: 16GB
• Storage: 20GB free

Recommended:

• GPU: 12GB+ VRAM (RTX 3060, 4070, etc.)
• RAM: 32GB
• Storage: 50GB SSD

Optimal:

• GPU: 24GB VRAM (RTX 4090, A5000, etc.)
• RAM: 64GB
• Storage: 100GB+ NVMe

Software Requirements

• Python 3.10
• CUDA (for NVIDIA GPUs)
• Training tool (kohya_ss recommended)
• Image editing software (optional)

Cloud Alternatives

If local hardware is insufficient:

• Google Colab (limited, free tier exists)
• RunPod (GPU rentals)
• Vast.ai (spot instances)
• Civitai (hosted training)

Dataset Preparation

The most important factor in LoRA quality is dataset quality. Better images = better results.

Image Requirements

Quantity:

• Minimum: 10 images
• Recommended: 15-30 images
• Maximum: 50+ (diminishing returns)

Quality:

• High resolution (512x512 minimum, 1024x1024 preferred)
• Sharp, well-lit
• Consistent subject
• Varied poses/angles

What to Include:

• Different angles (front, side, 3/4)
• Different expressions
• Different lighting conditions
• Varied backgrounds
• Range of poses

What to Avoid:

• Blurry images
• Heavy filters/effects
• Extreme crops
• Duplicate or near-duplicate images
• Low resolution

Captioning Images

Each image needs a caption describing what's in it.

Manual captioning: Write descriptions for each image:

a woman with brown hair, wearing a blue dress, standing in a garden, sunny day

Automatic captioning: Use tools like BLIP, WD Tagger, or Florence-2 to generate base captions.

Best practice: Auto-caption, then manually review and refine.

Caption Structure

Include:

• Subject description
• Clothing/appearance
• Setting/background
• Lighting/mood
• Style descriptors (if style LoRA)

Use trigger word: A unique identifier that activates your LoRA.

Example with trigger "sarahmodel":

sarahmodel, a woman with brown hair, wearing casual clothes, coffee shop background

Dataset Organization

Create folder structure:

training_data/
  └── 10_sarahmodel/
      ├── image001.png
      ├── image001.txt
      ├── image002.png
      ├── image002.txt
      ...

The "10_" prefix indicates how many training repeats per image.

Setting Up kohya_ss

kohya_ss is the most popular LoRA training tool. Here's how to set it up.

Installation (Windows)

1. Download from GitHub
2. Run setup.bat
3. Wait for dependencies to install
4. Run gui.bat to start interface

Installation (Linux)

git clone https://github.com/bmaltais/kohya_ss.git
cd kohya_ss
./setup.sh
./gui.sh

Interface Overview

kohya_ss has tabs for different functions:

• Dreambooth: For full model training
• LoRA: For LoRA training (use this)
• Textual Inversion: For embedding training
• Utilities: Dataset tools

Alternative Tools

• OneTrainer: More beginner-friendly interface
• LoRA Easy Training Scripts: Command-line based
• Civitai Training: Cloud-based, simplest option

Training Your First LoRA

Step-by-Step Process

Step 1: Prepare dataset

• Gather 15-30 quality images
• Caption each image
• Organize in folder structure

Step 2: Configure training

• Open kohya_ss LoRA tab
• Set paths to your data
• Choose base model (SDXL or SD 1.5)
• Set training parameters (see below)

Step 3: Start training

• Click train button
• Monitor progress
• Wait for completion (1-4 hours typically)

Step 4: Test results

• Load LoRA in ComfyUI or A1111
• Generate with trigger word
• Evaluate quality

Step 5: Iterate

• If results poor, adjust parameters
• Retrain if needed
• Fine-tune with additional images

Essential Training Parameters

Network Rank (dim):

• Controls LoRA capacity
• Low (8-16): Fast training, simpler concepts
• Medium (32-64): Balanced, most use cases
• High (128+): Complex concepts, longer training
• Start with: 32

Network Alpha:

• Regularization parameter
• Usually set equal to or half of rank
• Start with: 16-32

Learning Rate:

• How fast the model learns
• Too high: Overfitting, artifacts
• Too low: Undertrained
• Start with: 1e-4 (0.0001)

Epochs:

• Full passes through dataset
• More epochs = more training
• Start with: 10-20

Batch Size:

• Images processed together
• Higher = faster but needs more VRAM
• Start with: 1-2 (8GB), 4+ (24GB)

SDXL vs SD 1.5

SD 1.5 LoRAs:

• Faster to train
• More compatible
• Smaller ecosystem going forward

SDXL LoRAs:

• Higher quality potential
• Longer training
• Future-focused

Recommendation: Train SDXL if possible for better results.

Testing Your LoRA

Loading in ComfyUI

1. Place LoRA file in models/loras/
2. Add Load LoRA node
3. Connect to model and CLIP
4. Use trigger word in prompt
5. Generate

Evaluation Criteria

Good signs:

• Recognizable subject
• Works with various prompts
• Doesn't override other prompt elements
• Consistent quality

Bad signs:

• Artifacts and distortions
• Ignores other prompt elements
• Only works with specific prompts
• Inconsistent results

Strength Settings

• 0.5-0.7: Subtle influence
• 0.7-0.9: Standard use
• 0.9-1.0: Strong influence
• 1.0+: May cause artifacts

Start at 0.7-0.8 and adjust based on results.

Troubleshooting Common Issues

LoRA Not Working

Symptoms: No visible effect when applied Causes:

• Trigger word not in prompt
• LoRA strength too low
• Wrong base model type

Solutions:

• Add trigger word to prompt
• Increase strength to 0.9+
• Verify LoRA matches base model (SDXL vs SD1.5)

Overfitting

Symptoms: Only produces training images, ignores prompts Causes:

• Too many epochs
• Learning rate too high
• Dataset too small

Solutions:

• Reduce epochs
• Lower learning rate
• Add more varied images

Underfitting

Symptoms: LoRA barely affects output Causes:

• Too few epochs
• Learning rate too low
• Insufficient training data

Solutions:

• Increase epochs
• Raise learning rate slightly
• Improve dataset quality

Style Bleeding

Symptoms: LoRA style affects all generations Causes:

• Captions too generic
• No regularization images
• High strength

Solutions:

• More specific captions with trigger
• Add regularization (class images)
• Lower LoRA strength

Artifacts and Distortion

Symptoms: Faces distorted, colors wrong Causes:

• Network rank too high
• Learning rate too aggressive
• Bad training images

Solutions:

• Lower network rank
• Reduce learning rate
• Review dataset quality

Advanced Topics

Multiple Concept LoRAs

Train LoRAs with multiple trigger words for different concepts in one file.

Structure:

20_character1/
15_character2/
10_style/

Each folder trains its own concept with separate trigger words.

Regularization Images

Additional images that prevent the LoRA from forgetting the base model's capabilities.

When to use:

• Training specific faces
• Narrow concept LoRAs
• Preventing style bleeding

LoRA Networks Types

LoCon (LoRA for Convolution):

• More thorough training
• Better for styles
• Larger file size

LoHA (LoRA with Hadamard Product):

• Experimental
• Sometimes better for complex concepts

Standard LoRA:

• Most compatible
• Good default choice

Merging LoRAs

Combine multiple LoRAs into one:

• Reduces file management
• Can improve consistency
• Risk of quality loss

Use merge tools in kohya_ss or ComfyUI.

Best Practices Summary

Dataset Best Practices

1. Quality over quantity (15 great images beat 50 mediocre ones)
2. Variety in poses, angles, lighting
3. Consistent subject across images
4. Clear, detailed captions
5. Use unique trigger word

Training Best Practices

1. Start with conservative settings
2. Train for fewer epochs initially
3. Test frequently during training
4. Save checkpoints to compare
5. Document settings for reproduction

Usage Best Practices

1. Start with moderate strength (0.7-0.8)
2. Include trigger word in prompt
3. Test with varied prompts
4. Combine with other LoRAs carefully
5. Match LoRA to base model type

Frequently Asked Questions

How long does LoRA training take?

With 20 images on an RTX 4090: 30-60 minutes With 20 images on an RTX 3060: 2-4 hours Cloud training varies by service

Can I train on copyrighted images?

Legal gray area. For personal use, generally accepted. Commercial use requires careful consideration of training data rights.

How many images do I really need?

10 is minimum, 20-30 is ideal. More than 50 rarely helps and can hurt.

Can I train on my own face?

Yes, this is a common use case. Be aware of potential misuse if sharing publicly.

Do I need to credit when using LoRAs?

Check the LoRA's license. Some require attribution, others don't.

Can I sell images made with LoRAs?

Generally yes for your own LoRAs. Check licenses for others' LoRAs.

What's the difference between LoRA and Dreambooth?

LoRA produces small adapter files. Dreambooth produces full model fine-tunes. LoRA is more practical for most uses.

Can I train LoRAs for Flux?

Yes, tools and support exist. Process is similar but requires Flux-specific settings.

How do I share my LoRA?

Upload to Civitai or Hugging Face with proper documentation and example images.

Wrapping Up

LoRA training transforms AI image generation from general-purpose to personally customized. With good training data and patience, you can create LoRAs that capture specific characters, styles, or concepts with remarkable consistency.

Key takeaways:

• Dataset quality is paramount
• Start with conservative settings
• Test and iterate
• Match LoRA to base model type
• Document your process

Your first LoRA might not be perfect. That's normal. Each training teaches you more about what works for your specific use case.

For hands-on practice without local setup, Apatero.com provides tools for AI generation. For advanced LoRA techniques, see our kohya_ss guide.

Quick Reference: Training Checklist

Before Training:

• 15-30 quality images collected
• Images properly cropped and sized
• Captions written for each image
• Trigger word chosen
• Training tool installed

During Training:

• Base model selected (SDXL or SD1.5)
• Network rank set (32 recommended)
• Learning rate configured (1e-4)
• Epochs determined (10-20)
• Training started and monitored

After Training:

• LoRA tested in generation tool
• Trigger word verified working
• Optimal strength determined
• Settings documented for future reference

Recommended Training Settings

Start with "Beginner Safe" and adjust based on results. Happy training!