You can run this on an M2 Mac Studio. That’ll be consumer level hardware in a few years.
…until I tried the same on my RTX 4070 and it made my Mac look like a joke.
For the 30 seconds my Mac would have taken for 1 result, which will probably need revising, the RTX would give me 30 results.
However the RTX was half the cost of my Mac, so it’s not a good investment if I just want to generate some images. I’d rather pay for the cloud if I didn’t have the RTX already.
The actual best image AI, midjourney, is probably a gigantic model under the hood, that takes 8 A100s to run (Aka more than 100GB VRAM). That's why their quality is leaps and bounds above stable diffusion XL, its because the model size simply allows for it.
Model sizes continuously grow to exploit the available hardware to the limit. Midjourney and GPT-4 have both proven that model quality is decisive to success and paying customers, so consumer hardware can never catchup to whatever Nvidia sells to the cloud.
Do you have any source for this speculation? In my experience image models are always much smaller than language and even the largest llama will fit in a smaller GPU machine than that.
Unet are really expensive to run compare to a regular GPT model and they are compute-bound thx to convolution, a reason why no one has trained a unet that comes close to consume 100GB of VRAM during inference. I doubt that MJ is much bigger than SD XL, it's good but not revolutionary.
For me it kicks the shit out of Midjourney in flexibility and quality. I can make more images of higher quality, faster and cheaper.
An optimized XL model in the hands of an expert beats it, handily.
And Adobe sells to experts, not consumers for the most part.