My guess is that you didn’t remove the nsfw filter

Oops, true. But it kinda is? 😅

If you get it to “predict” the next image it will give the mean image. This is because the previous image contains no information about the next image.

For image generation GAN modes and auto encoder models are the basic way to generate fake images.

After that I’m not sure how to extend it to a video context though.

I’m just making a single picture now (got to be some picture after learning from 3.5gb of examples)

If I were to make a video, I suppose I could just try to get the same model to regenerate each frame of a sample video?

Well, if you get completely black images, then there is something fundamentally wrong. But even if it would work, you couldn’t expect so much from it regardless of the implementation, because it seems like you are trying to teach it to predict a transition pattern (even worse if its randomised). It also makes me wonder what your loss function was.

Either ways, as u/_Yeet_xoxo pointed out, you might want to give a try to gans

After that run lstm/rnn on sequence of latent variable obtained from autoencoder to predict next latent variable and decode that maybe.

Turn all image into latent space run pca on these latant variable and look if there is predictable pattern , if there is then decode it.

There is a fundamental problem. Try having it predict an image after zero examples. This should give you a noisy image from the randomized seed. Then try after 100 examples, 1000, 10,000, etc. My guess is your input images are incorrect so it’s learning to predict all 0s or black. Is the accuracy super high?

I’m trying not to convert the images into numpy arrays before having the models loop through them. I think that could be causing the problem.

As for accuracy, I don’t think so? 99%

You don’t think 99% is a super high accuracy?

Your input has to match what the model is expecting. It sounds like this is your problem