Phase 2: Image Generation · 60 min · Python · kohya_ss · ai-toolkit

LoRA Fine-Tuning for Brand Consistency

LoRA is how you make generative media consistent — the difference between 'nice image' and 'on-brand production output'.

Hiring signal: LoRA training for brand consistency is one of the most in-demand skills in generative media — it's the core of every production image generation service.

What you will learn

Prepare a LoRA training dataset: 50-500 images, captioning, cropping, quality filtering
Run LoRA training scripts (kohya_ss or ai-toolkit) with appropriate hyperparameters
Tune hyperparameters: rank, alpha, learning rate, training steps
Implement LoRA stacking: combining multiple LoRAs in a single generation with weight blending

The Problem

A company uses FLUX.1 Schnell for their marketing images. The images look good, but they don't look on-brand. Each image has a different style — different color grading, different lighting, different texture. The marketing team says "these don't look like our brand." The team tries prompt engineering ("in the style of our brand...") — it doesn't work because the model doesn't know what "our brand" looks like.

The solution: train a LoRA on 50-100 images of the brand's visual style. Now every generation has consistent brand aesthetics. This is the core of every production image generation service.

What you'll build
Prepare a small dataset (20-50 images of a consistent style), train a LoRA adapter using ai-toolkit or kohya_ss, and generate images with and without the LoRA to demonstrate brand consistency. Document the training configuration and results.

What is LoRA?

LoRA (Low-Rank Adaptation) adds small trainable matrices to a model's weights instead of fine-tuning the entire model. This makes training:

Fast: hours instead of days (on a single GPU)
Cheap: trains on a single A100/H100
Small: output is 50-300MB instead of GBs
Stackable: multiple LoRAs can be combined

How LoRA Works

Original weight: W (e.g., 4096 × 4096 = 16.7M parameters)

LoRA decomposition:
  W' = W + A × B
  where A is (4096 × r) and B is (r × 4096)
  with rank r = 16: A + B = 65,536 + 65,536 = 131,072 parameters

Reduction: 16.7M → 131K (0.8% of original)

The rank r controls the LoRA's capacity:

r=8: minimal style change, smallest file
r=16: standard, good balance (most LoRAs)
r=32: stronger style, larger file
r=64: maximum style, largest file

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers Dataset Preparation, Training with ai-toolkit, Training with kohya_ss, Key Hyperparameters, LoRA Stacking, Evaluating LoRA Quality, Key Takeaways, What's Next — plus a hands-on lab, quiz, and project artifact.

Create a free account to unlock Phase 0 and Phase 1 of every course — no credit card.