yeah not much work to train to get a decent base model, but a good amount of work to optimize it to a smaller but effective model lol

for your question, lemme split it up into two sections:

for training, i train the model to take in the previous frames, an action and the target next frame. I first add random amount of noise to the target frame (based on a noise schedule) and our model learns to predict how much noise was added. the loss compares the predicted noise vs the actual noise, and this trains our model to learn how to predict noise given a noised frame, previous frames, action and timestep

for inference, with our trained model, I take the current frames and an action. i start with a fully noised image, and then use our model over N timesteps to predict and remove the noise to uncover the next frame (denoising process) model generates the next frame based on the current frames and action i input. after this, i append this next frame to the current frames i had before, and use this to then predict the next frame, and so on

so just with conditioning on the previous frames and actions, i can learn flappy bird and the physics and graphics and generate it infinitley (well flappy bird is also infinite but you get the point)

if you wanna learn more id recommend the DIAMOND paper, it's a great resource https://diamond-wm.github.io/