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'.

Real-world application: 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

Objective

Produce and compare image candidates against an explicit creative brief. In the paired studio lab, prepare a small dataset (20-50 images of a consistent style/brand), 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.

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.

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You've read the first 2 sections. The rest of this lesson covers The Concept, Build, Check Yourself, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.

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