Kind of. Speaking very generally, it's not just the number of weights ("parameters") but how you use them. The architecture of the neural network is where the

Kind of. Speaking very generally, it's not just the number of weights ("parameters") but how you use them. The architecture of the neural network is where the most interesting new work is taking place. Stable Diffusion, which was beating out Dall-E2, was a big deal not just because the weights were public, but also because they were small enough to run on consumer hardware. This is because of the clever architectural choice (plus some other methods.) This was surprising to me, because most of the interesting image generation required huge models running on commercial GPUs. You'd generally expect diminishing returns from adding weights (as well as a lot more training time and maybe more data) but this is not a law.