Lesson Content

What fine-tuning does.

Fine-tuning trains a model on a specific dataset of images – teaching the model to produce output consistent with the visual style, subject appearance, or aesthetic character of those training images. A fine-tuned model on a brand's visual library produces new images that match the brand's established aesthetic without requiring extensive style descriptor prompting. Fine-tuning is appropriate when a specific, repeatable aesthetic is needed that can't be achieved reliably through prompting alone.

Dataset preparation for fine-tuning.

Training dataset requirements: high-quality images that clearly represent the target style or subject; sufficient quantity (typically 15-50+ high-quality examples for effective fine-tuning – verify current Leonardo AI requirements); consistent style or subject across the dataset (mixed styles produce inconsistent fine-tuned results); and appropriate rights to use the training images (only use images you own or have explicit rights to train on).

Initiating fine-tune training.

Leonardo AI provides a fine-tuning interface where training images are uploaded, training parameters are configured, and the training job is initiated. Training jobs take variable time depending on dataset size and server load. The trained model is then available in the model selector for generation. Verify the current fine-tuning interface and options at leonardo.ai.

Evaluating fine-tuned model output.

After training, evaluate the fine-tuned model with test prompts across multiple subjects to verify it produces the intended consistent aesthetic: does it reliably reproduce the target style? does it generalize appropriately to new subjects not in the training set? does it maintain quality across different prompt variations? Document evaluation results before committing the model to production use.