LoRA & Fine-Tuning: Customizing Models

01What Is LoRA?

LoRA (Low-Rank Adaptation) is a technique to efficiently adapt pre-trained AI models without having to retrain the entire model. Instead of adjusting all billions of parameters, LoRA modifies only a small portion – typically 1-10% of the parameters. The result is small files (10-200 MB) that can be applied to a base model.

02What Are LoRAs Used For?

Typical use cases for LoRAs:

• Style Transfer: Train a specific artistic style (e.g., anime, oil painting, pixel art)
• Character Consistency: Train a face or character for consistent representations
• Concepts: Teach the model new concepts (e.g., a specific product, logo, or architectural style)
• Quality Improvement: LoRAs can improve overall image quality or specific details
• Pose & Composition: Train specific poses, camera perspectives, or compositions

03Using LoRAs in ComfyUI

In ComfyUI, use the 'Load LoRA' node to apply a LoRA to a model. You can control the strength of the LoRA via the 'strength' parameter (typical: 0.5–1.0). Multiple LoRAs can be used simultaneously by applying them sequentially to the model. Place LoRA files in the 'models/loras/' folder of your ComfyUI installation.

04Training Your Own LoRAs

To train your own LoRAs you need: 10-50 high-quality training images, a training tool like Kohya_ss or LoRA Easy Training Scripts, and some patience. Training takes between 30 minutes and several hours depending on the dataset and GPU. Important parameters are the learning rate, the number of epochs, and the rank value (affects LoRA size and detail accuracy).

05Tips for Good LoRA Training

Follow these best practices:

• Quality over quantity: 20 perfect images are better than 100 mediocre ones
• Diverse training data: Different angles, lighting conditions, and backgrounds
• Correct captions: Use an auto-tagger like WD14 or BLIP for accurate descriptions
• Don't overtrain: Too many epochs lead to 'overfitting' – the model only generates the training images
• Use regularization: Class images prevent the base model from losing its general capabilities

06LoRA Sources