Start Here
This course is written for people who make things — illustrators, designers, photographers, art directors, animators, musicians, hobbyists. It assumes you want to *use* these tools well, not build them. There is no maths here, no code, and nothing you need to install.
Everything is organised around four questions: what actually happens when you type a description, how to write descriptions that get you what you pictured, which tool to reach for on a given job, and what you're allowed to do with the result once you have it.
What you'll be able to do by the end
- Explain, in plain language, what the machine is doing between your prompt and your picture — and use that to debug bad results instead of guessing
- Write prompts in whichever of the two prompting dialects your chosen tool speaks
- Pick the right tool for a job in under a minute, and know roughly what it will cost you
- Steer a result with reference images, masks and edits rather than rerolling the dice
- Answer a client asking "do I own this, and can we ship it?"
What is deliberately not here
Model architectures, U-Nets, latent spaces, LoRA training, ComfyUI graphs, VRAM budgets and local installation. None of that makes you a better artist, and all of it is a distraction while you're learning to see.
If you later want it — and some of you will — it lives in Deep Learning › Diffusion Models, where it belongs alongside the rest of the theory.
One thing to hold onto before you start
These tools do not understand your idea. They have seen an enormous number of images with words attached, and they are very good at producing something statistically consistent with the words you gave them. Every technique in this course is really the same technique: give it words and references that narrow down what it could plausibly produce until the only plausible answers are the ones you'd be happy with.