Requirements
What your computer needs.
Generation runs on the graphics card, so VRAM is the number that decides what a machine can do — more than the processor, more than system memory. We would rather tell you that now than on the second evening.
Linux is welcome and works well, but we teach the Windows path live.
The CPU loads and orchestrates; it is rarely the bottleneck.
16 GB is the comfortable floor. At 8 GB you will close other applications while you render.
This is the one that matters. VRAM sets your maximum resolution and whether video is realistic to run at home.
Model files are 2–24 GB each and you will collect a lot of them. An SSD roughly halves load times.
For the live sessions and for the first week of model downloads.
Will my machine run this?
Graphics memory is what decides whether the work runs at home. If you are not sure, choose “I do not know” — we check it with you before Day 1 either way.
On a Mac?
Apple Silicon (M1 through M4) runs image generation well from 16 GB of unified memory upward. Video generation is possible but slow — most Mac students on the course render video on a rented GPU and keep everything else local.
AMD or Intel Arc graphics?
They work, through ROCm or DirectML, and we will help you set it up. Be realistic: the tooling assumes NVIDIA, so expect a rougher install and fewer working nodes than the rest of the cohort.
No suitable machine yet?
You are not blocked, and you should still join. On Day 2 we set you up on a rented cloud GPU that you pay for by the hour and switch off when you are done — far cheaper than a monthly subscription, and everything else in the course is identical. Many students start there and move local once the work starts paying.