The Art of Consistency: Training Your First Face Model
Elena S.
AI Art Director & LoRA Specialist.
Generative AI models like Stable Diffusion (which powers Leonardo) are trained on billions of images. They know what a "woman" looks like generally, but they don't know your client, yourself, or your specific character. This is where Fine-Tuning comes in.
1. The Dataset is King
The quality of your output is mathematically limited by the quality of your input. Garbage in, garbage out. For a face model, you need:
❌ Bad Dataset
Selfies with filters, sunglasses covering eyes, group photos, low resolution, same angle in every shot.
✔️ Good Dataset
15-20 High Res photos, different lighting (day/studio), different angles (profile/front), strictly one person.
2. Understanding LoRA (Low-Rank Adaptation)
Training a full model (Checkpoint) takes massive computing power. A LoRA is a small file (adapter) that sits on top of the base model. It nudges the AI's weights just enough to recognize your subject without relearning everything else.
3. The Trigger Word
The Instance Prompt (e.g., "sks woman") creates a unique hook in the AI's latent space. If you just train it on the word "woman", you will corrupt the AI's understanding of all women. By using "sks" (a rare token), you create a safe pocket for your specific face.
Pro Tip: Don't overtrain. If your model starts producing deep-fried, high-contrast images, you have too many "Epochs" (training cycles).