LoRAs and Fine-Tuning, Explained

The elevator pitch

A LoRA is a small file (usually 50–200 MB) that patches a base image model to specialize in one thing — a specific person's face, a specific art style, a specific character. You load it on top of the base model at generation time.

A full fine-tune modifies the base model itself. Bigger file (2–7 GB), harder to train, harder to combine.

When to use a LoRA

• You want a specific character to appear consistently
• You want an artist's style you can't get from prompting alone
• You need a specific object (a real product, a real logo) rendered accurately
• You want to layer 2–3 concepts together

When to skip LoRAs entirely

• You can describe what you want in words → use prompting first, it's free
• You want something the base model already does well
• You don't have 30–100 clean reference images

Training basics

Modern SDXL LoRA training needs:

• 20–100 reference images at 1024×1024 (more = better up to ~100)
• Consistent style / subject across images
• Captions (short, factual descriptions) for each
• A GPU with 16+ GB VRAM, or a service that rents one

Training takes 1–4 hours on a 4090. Cost: usually $2–10 rented.

The stacking trick

You can load multiple LoRAs at once, each with its own weight (0.0–1.0). A common recipe: character LoRA at 0.8 + style LoRA at 0.6. Play with weights until it looks right.

Imagoat's approach

Imagoat's Character Studio (coming soon) handles the training pipeline for you — upload references, get a character back that you can drop into any generation. No .safetensors files, no scripts.