The neural network always tries to predict the final image, but the diffusion process takes the vector and shrinks it, then turns it into a distribution by adding Gaussian noise, so if the model makes a decision, it may not be the final one.

Edit: I thought you were concerned about the model changing decision; the model has a defined amount of steps it can take, and this affects the amount that the diffusion process can shrink the vector from the Unet (the neural network).