[tomohelix]

Likely training and running cost.

Most AI art generators are run on single GPU and can be trained with some top-of-the-line consumer hardware. Expensive but accessible.

A full blown LLM like ChatGPT is literally the cost of a small startup to build and trained. Running it is near impossible without cards like A100 which alone costs more than a full enthusiast grade PC.

Maybe eventually they will distill and optimize the models so that we can fit these things on a PC, then laptop, and then phones. But for now it is exclusively the domain of big tech.

[MuffinFlavored]

> Likely training and running cost.

Why wasn't this a problem for StableDiffusion vs DALL-E?

[tomohelix]

They are smaller models with less parameters. Their original small sizes relative to LLMs also let people play around with it and tune it to run on less expensive hardware, if the weights are given, ie open source like SD.

Originally SD was quite hard to run, with an 8GB high end card only outputting 256x256 images. Then AMD and NVIDIA started releasing 16GB and 24GB consumer cards and people start doing training on those GPUs and tuning their own models. Now we have plenty of cards and models that can do 512x512.

[MuffinFlavored]

> They are smaller models with less parameters.

I wouldn't have guessed image is a smaller model/easier to manipulate/generate than text.

[OmarAssadi]

Stable Diffusion will run on any decent gaming GPU or a modern MacBook, meanwhile LLMs comparable to GPT-3/ChatGPT have had pretty insane memory requirements - e.g., <https://github.com/facebookresearch/metaseq/issues/146>

[brookst]

Worth noting that the M-series macbooks are UMA so 100GB VRAM is costly but easily accessible. Their GPU performance is nowhere near a 96GB A100, but for sheer VRAM it’s a good choice.