Guides5 min read
When to Use a LoRA vs a Fixed Model
An honest decision rule for choosing training, references, or a simpler fixed-model workflow.
Start with the output requirement
Use references and a fixed model when you need speed, broad scene variety, and moderate consistency. Training becomes useful when identity precision and repeatability matter more than setup time.
Count the content volume
A small campaign rarely justifies training. A long-running persona, repeated client work, or a local generation pipeline can justify the dataset and testing cost.
Treat training as infrastructure
A LoRA is not a magic identity switch. It needs a clean dataset, repeatable prompting, model compatibility checks, and quality control across every new workflow.
Put the guide into practice
Browse a pack and copy a prompt structure into your own workflow.