You can think of it more like this:
If I do 100 experiments of dropping stones at variable heights and measuring the time it takes for the stone to land on the ground I have enough datapoints to make a linear estimation of gravity by using linear regression. So based on my data I create a model that the time it takes for a stone to fall is sqrt(2h/9.81). Now if you want to figure out how long it takes for your stones to fall, you don’t need to redo all the experiments and can instead rely on the parameters I give you (say 9.81 in this case) to calculate it yourself.
With these models it works exactly the same way. Someone dropped millions of rocks and created a formula of unbelievable complexity and what they now did is they released that formula with all their calculated parameters into the world. What you do when you ultimately use Stable Diffusion is you just calculate the result of this formula and that is your image. You never have to process those images.
With these models it works exactly the same way. Someone dropped millions of rocks and created a formula of unbelievable complexity and what they now did is they released that formula with all their calculated parameters into the world. What you do when you ultimately use Stable Diffusion is you just calculate the result of this formula and that is your image. You never have to process those images.