A Practical LoRA and Dreambooth Workflow for Personal Fine-Tuning

Fine-tuning a generative model is a series of small decisions, and most of them matter. The dataset you select, the captions you write, the training parameters you choose, and the way you evaluate the results all shape whether the final model captures what you intended. This course walks through a practical workflow from start to finish. You will work through written exercises that mirror a real fine-tuning project, including dataset curation, captioning, training, and iteration. The emphasis is on developing judgment about what to try next when the first result is not quite right. What you'll learn: - Curate datasets for style, subject, or concept fine-tuning with clear quality standards - Write captions and tags that guide the model toward what you actually want to capture - Choose between LoRA, Dreambooth, and other techniques based on your goal and resources - Plan training parameters including learning rate, steps, and regularization for stable results - Evaluate results against your original intent with structured comparisons rather than impressions - Iterate intelligently by changing one variable at a time and keeping a clear log of what you tried The course progresses from dataset preparation through training, evaluation, and iteration. A capstone written exercise asks you to draft a complete workflow plan for fine-tuning a model on a style or subject of your own choosing. This course is designed for beginners with no fine-tuning experience but some familiarity with generative model tools, including digital artists, designers, and hobbyist developers. No deep machine learning knowledge is required. The course treats fine-tuning as a craft you can learn through structured practice.