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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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