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naraburns  nihil supernum  1yr ago (text post)   35417 thread views

Culture War Roundup for the week of October 3, 2022

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This week's revolutionary AI advance:

Imagen Video

It's not really revolutionary, as people have been pointing out this is the obvious next step for ages months now. But it still is a milestone worth noting.

As for this:

While our internal testing suggest much of explicit and violent content can be filtered out, there still exists social biases and stereotypes which are challenging to detect and filter. We have decided not to release the Imagen Video model or its source code until these concerns are mitigated.

Google's made a habit of this. They announce an amazing advance, and then say no one can have access to it because it can be used for Evil. No matter: Stable Diffusion will have something comparable out in a couple months.

ETA:

Actually, this out of DeepMind might be the bigger advance today, if less flashy:

Press: Discovering Novel Algorithms with AlphaTensor

Paper: Discovering faster matrix multiplication algorithms with reinforcement learning

My greatest fear for AI content generation is it being dominated by woke megacorps, with independent creators permanently locked out of contributing to culture. It looks like Google is investing heavily in that dystopia.

Novelai and stable diffusion being mostly uncensored has been a big white pill so far, but it feels like the shoe is about to drop.

There is an easier solution to this, AIs can be trained by anyone with enough computing power and training AI isn't that expensive. AI is actually fairly democratic as everyone can make their own. Once the cat is out of the box it isn't hard for everyone to get it. Information spreads naturally. The big risk is big corporations and governments access to data to use the AI on. That can give them a tremendous advantage.

Tuning is relatively cheap, but initial training is (currently) expensive. The furry StableDiffusion tweaks probably cost 50-400 USD depending on vendor and management, but the initial StableDiffusion model they're based on reflects ~300k USD at official prices (although probably got at least some bulk discounting).

Some of that'll go down as GPU prices decrease and newer equipment becomes available, but there are some costs for bandwidth and energy that are slower to change. This might go from 'old condo' to 'new car', but it's not likely to go to 'vacation' or 'a couple weeks' savings' for a few years, maybe even the better part of a decade, without dramatic changes to the underlying code.

For data, it varies more. LAOIN's a lot of bandwidth, curation, and drive space, but it's... actually not that incredible for a single (if slightly nuts) person. Other data sources, probably less so, either due to scale (eg video), to availability (eg privacy), or to more esoteric causes (AI music is a legal clusterfuck).

This might go from 'old condo' to 'new car', but it's not likely to go to 'vacation' or 'a couple weeks' savings' for a few years, maybe even the better part of a decade, without dramatic changes to the underlying code.

Completely naïve question: Would it be plausible to rig up something distributed, like the seti@home in days of yore or (shudder) crypto-farming?

To a limited extent. Several training tools (eg W&B) have built-in distributed training capabilities, although these are generally intended for local networks. There are some tradeoffs, though. Even small datasets are 100+GB (eg, the 200k images uses to tune the furry branch) and LAION is 80TB for the curated data, plus a bit more for tag info. You're not going to distribute that full set to every volunteer (might not even train on it!), but it's a scope of the bandwidth costs. Synchronization at that epoch size isn't hugely expensive, but it does slow you down and/or waste power depending on approach.

Unfortunately, the biggest problem is that models have minimum VRAM requirements to run even at a batch size of 1, and these amounts are pretty high at the cutting edge. The original CompVis version of Stable Diffusion required 20+GB of VRAM to train, and this largely limited it to 10k USD or higher specialist 'tensor core' gpus, which largely meant there'd be no @home to distribute to. There's some wiggle room here related to how you code the training, what level of precision you use, and how some averaging and back-propagation happens, and I've heard people suspect they might be able to get full training of current StableDiffusion around 8GB (right now, only textual inversion and tuning is implemented at that range, but the optimization should generalize), albeit at large CPU-RAM and small-but-significant performance costs. Which gets to some consumer-grade GPUs, but not a ton. It's possible people would come up with better optimizations than even that were there no alternative, but I'm skeptical that there'll be the demand now, between Google Colab and nVidia 3090s being available.

And that amount scales both with parameter count and training image resolution. It's suspected that at least part of the better output quality from NovelAI comes from their ability to train on uncropped data, rather than just 512px by 512px cropped or downscaled images, but this bloats run requirements out further.

Enthusiasts are unlikely to want to make huge models anyway since inference (ie, running the model) has similar-if-smaller VRAM requirements, but at least for image generation it looks like the minimum sweet spot is at least 6GB runtime inference.

Other clever stuff may run into similar problems... there's a fascinating 2d-3d analysis package at GET3D, but in addition to limits on accessing the pretrained model, probably requires all 16GB to run or train at any speed. There's probably some unexplored low-hanging fruit, but there's also probably a lot of clever-but-inaccessible stuff.

Does FSDP help at all here? My very naive understanding is that its approach allows sharding of the model parameters so that they don't have to all fit into VRAM, though I wouldn't be surprised if it couldn't scale down to arbitrarily low VRAM or scale up to arbitrary numbers of parameters. Perhaps a similar strategy could be used for wide scale distributed learning on consumer hardware.

I believe so, but I've not looked too closely at that tech to know what its limits are. From a quick glance, it seems likely that there would be some CPU-RAM, performance, and synchronization costs. But it likely could lower the floor.

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