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You should get a Nvidia card with as much VRAM as possible. A 12 GB RTX 3060 is probably the most cost efficient at the moment. I don't think AMD is really via

You should get a Nvidia card with as much VRAM as possible. A 12 GB RTX 3060 is probably the most cost efficient at the moment. I don't think AMD is really viable for ML. Nvidia has the mind share in that segment, so nearly all tools will work with Nvidia, while very few support AMD.

hedora 4y ago
Not disagreeing, but my < $300 8GB AMD runs stable diffusion just fine.
- capableweb 4y ago
  Guessing "just fine" is relative, but care to put a prompt + the settings you use, and how many it/s you get? I'm getting max ~8it/s on a 2080ti and I think even that feels slow sometimes so looking to upgrade my GPU now, curious to see what "just fine" means for you here.
  - hedora 4y ago
    100 seconds per batch of five (1.7it/s); default settings (512x512, etc) Performance seems to be prompt-independent; I'm using a docker container that seems to have disappeared, this modified version for amd cards with less than 10gb: https://github.com/basujindal/stable-diffusion https://github.com/basujindal/stable-diffusion and this workaround: https://github.com/RadeonOpenCompute/ROCm/issues/1756#issuecomment-1160386571 https://github.com/RadeonOpenCompute/ROCm/issues/1756#issuec... Anyway, don't "upgrade" to my budget AMD card. :-)
    - capableweb 4y ago
      > 100 seconds per batch of five (1.7it/s); default settings (512x512, etc) What settings precisely? Scale, steps and sampler being the most important ones (together with size, but you already shared that :) )
- Uehreka 4y ago
  Stable Diffusion is the kind of phenomenon where people are actually contributing other backends (like the one for the M1 GPU). That’s not super common though, a lot of the time if you want to get a network running you need an Nvidia card so you can use CUDA (it’s not even about hardware performance, just that CUDA and CUDNN and so on are written by Nvidia for their GPUs).
  - paulgb 4y ago
    I have a dumb question: when someone implements a backend like mps for stable diffusion, what are they actually implementing? Shims for Nvidia proprietary stuff that doesn’t exist outside of CUDA?
- orbital-decay 4y ago
  Keep in mind that bigger models are coming. And to use all features of even the current SD version, you need a lot of VRAM - 12GB for textual inversion (making it learn your own style), 30GB+ for Dreambooth (sort of micro-finetuning that doesn't need a GPU farm and a huge tagged dataset), a lot for img2img on a high-res picture. It also massively benefits from the large amount of compute cores. Right now, the bigger and faster, the better. And there's really no limit of the computing power you can throw at various tasks to make them run better. It almost looks like 90s again.

hedora 4y ago

Not disagreeing, but my < $300 8GB AMD runs stable diffusion just fine.

capableweb 4y ago
Guessing "just fine" is relative, but care to put a prompt + the settings you use, and how many it/s you get? I'm getting max ~8it/s on a 2080ti and I think even that feels slow sometimes so looking to upgrade my GPU now, curious to see what "just fine" means for you here.
- hedora 4y ago
  100 seconds per batch of five (1.7it/s); default settings (512x512, etc) Performance seems to be prompt-independent; I'm using a docker container that seems to have disappeared, this modified version for amd cards with less than 10gb: https://github.com/basujindal/stable-diffusion https://github.com/basujindal/stable-diffusion and this workaround: https://github.com/RadeonOpenCompute/ROCm/issues/1756#issuecomment-1160386571 https://github.com/RadeonOpenCompute/ROCm/issues/1756#issuec... Anyway, don't "upgrade" to my budget AMD card. :-)
  - capableweb 4y ago
    > 100 seconds per batch of five (1.7it/s); default settings (512x512, etc) What settings precisely? Scale, steps and sampler being the most important ones (together with size, but you already shared that :) )
Uehreka 4y ago
Stable Diffusion is the kind of phenomenon where people are actually contributing other backends (like the one for the M1 GPU). That’s not super common though, a lot of the time if you want to get a network running you need an Nvidia card so you can use CUDA (it’s not even about hardware performance, just that CUDA and CUDNN and so on are written by Nvidia for their GPUs).
- paulgb 4y ago
  I have a dumb question: when someone implements a backend like mps for stable diffusion, what are they actually implementing? Shims for Nvidia proprietary stuff that doesn’t exist outside of CUDA?
orbital-decay 4y ago
Keep in mind that bigger models are coming. And to use all features of even the current SD version, you need a lot of VRAM - 12GB for textual inversion (making it learn your own style), 30GB+ for Dreambooth (sort of micro-finetuning that doesn't need a GPU farm and a huge tagged dataset), a lot for img2img on a high-res picture. It also massively benefits from the large amount of compute cores. Right now, the bigger and faster, the better. And there's really no limit of the computing power you can throw at various tasks to make them run better. It almost looks like 90s again.