[devadvance]

From the paper:

> In this work, we present the first text-to-image diffusion model that generates an image on mobile devices in less than 2 seconds. To achieve this, we mainly focus on improving the slow inference speed of the UNet and reducing the number of necessary denoising steps.

As a layman, it's impressive and surprising that there's so much room for optimization here, given the number of hands on folks in the OSS space.

> We propose a novel evolving training framework to obtain an efficient UNet that performs better than the original Stable Diffusion v1.52 while being significantly faster. We also introduce a data distillation pipeline to compress and accelerate the image decoder.

Pretty impressive.

[TeMPOraL]

> As a layman, it's impressive and surprising that there's so much room for optimization here, given the number of hands on folks in the OSS space.

There's only so many folks in OSS space that are capable of doing work from this angle. There are more who could be micro-optimizing code, but the most end up developing GUIs and app prototypes and ad-hoc Python scripts that use the models.

At the same time, the whole field moves at ridiculously fast pace. There's room for optimization because the new model generations are released pretty much as fast as they're developed and trained, without stopping to tune or optimize them.

Also, there must be room for optimization given how ridiculously compute-expensive training and inference still is. Part of my intuition here is that current models do roughly similar things to what our brains do, and brains manage to do these things fast with some 20-50 watts. Sure, there are a lot of differences between NN models and biological brains, but to a first approximation, this is a good lower bound.

[bee_rider]

It isn’t obvious to me that these models produce something similar to our brains’ output. We can imagine images of course, but the level of quality is hard to define, and it is really hard and time consuming to save the output of an imagined image.

People paint or draw imagined images, but that’s a slow process and there’s a feedback loop going on throughout the whole thing (paint a bit, see how it looks, try a little happy tree, didn’t work out, turn it into a cloud). If we include the time spent painting and reconsidering, image generation using humans is pretty expensive.

An iPhone battery holds tens of watt-hours. A painting might take hours to make (I don’t paint. A couple hours is quick, right?), so if the brain is burning tens of watts in that time, the total cost could be in the same ballpark as generating images until your battery dies. But of course it is really hard to make an apples-to-apples comparison here because the human spends a lot of energy just keeping the lights on while bandwidth is limited by the rate of arm-movement.

[wcarss]

I'm sad that Carmack decided to (as I understand it) focus away from LLMs because they're "already getting enough eyes" -- it feels like he wants to make a novel paradigm shift kind of contribution, but his magic power has always seemed to me to be a capability of grasping a huge amount of technical depth in detail, and seeing past the easy local optima to the real essence of the computation being done, and finding ways to measure and squeeze everything out of that.

Carmack could possibly get us realtime networked stable diffusion text to video and video to video at high resolution, maybe even on phones. It will probably happen anyway, but it might take 5+ extra years, and there'll probably be a ton of stupid things we never fix.