Scientists have built a framework that gives generative AI systems like DALL·E 3 and Stable Diffusion a major boost by condensing them into smaller models — without compromising their quality.
While I think the realism of some models is fantastic and the flexibility of others is great it is starting to feel like we’re reaching a plateau on quality. Most of the white papers I’ve seen posted lately are about speed or some alternate way of doing what ControlNet or inpainting can already do.
That’s maybe because we’ve reached the limits of what the current architecture of models can achieve on the current architecture of GPUs.
To create significantly better models without having a fundamentally new approach, you have to increase the model size. And if all accelerators accessible to you only offer, say, 24gb, you can’t grow infinitely. At least not within a reasonable timeframe.
While I think the realism of some models is fantastic and the flexibility of others is great it is starting to feel like we’re reaching a plateau on quality. Most of the white papers I’ve seen posted lately are about speed or some alternate way of doing what ControlNet or inpainting can already do.
That’s maybe because we’ve reached the limits of what the current architecture of models can achieve on the current architecture of GPUs.
To create significantly better models without having a fundamentally new approach, you have to increase the model size. And if all accelerators accessible to you only offer, say, 24gb, you can’t grow infinitely. At least not within a reasonable timeframe.