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Glossary · LoRA Training

What Is a Training Dataset for LoRA?

By Earngenix Team ·

⚡ Quick Answer

A training dataset is the folder of images — and their matching captions — you gather to train a LoRA. The model learns whatever style or subject those images show, so the quality and variety of your dataset directly decides how good the finished LoRA turns out.

A small set of clean, varied images almost always trains a better LoRA than a large pile of repetitive or low-quality ones.

Where You'll See It

It's simply a folder of images, usually with a matching .txt caption file for each one, sharing the same filename. Whatever LoRA training tool you use points at this folder as its starting material.

Quick Example

A dataset for a specific character LoRA might contain 20–30 images of that character across different poses, outfits, and lighting — enough variety that the LoRA learns the character's actual identity, not just one specific pose or photo.

Including too many near-identical images — the same pose, same background, same lighting — can cause the LoRA to "overfit," baking in that specific look rather than learning the subject in a way that generalizes to new prompts.

Frequently Asked Questions

For a specific character or person, 15–30 clear, varied images is a common starting range. For a broader style, you often want more, since a style has more visual variety to capture than one consistent subject.

No, but they should all be reasonably high quality and roughly consistent — the training process typically resizes images to a fixed resolution anyway, so extreme mismatches just mean more cropping or stretching.

You can, but it usually makes the LoRA less consistent, since it’s learning two different visual styles at once. It’s generally cleaner to keep a dataset to one consistent medium unless mixing styles is specifically your goal.

See It In Action

Ready to train your first LoRA?

Our LoRA guide covers using, and getting started with, LoRAs in ComfyUI.

Published: 2026-09-17 · Last updated: 2026-09-17

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