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Allow for using CPU if no CUDA device is detected #123
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Thanks, it works!
It is my understanding that by default, pytorch puts everything on the cpu and you have to specify .cuda()
or .to(torch.device("cuda")
to move things to the gpu.
So I think that maybe all the model.cpu()
calls are not needed. Same for .to(torch.device("cpu"))
Currently I am using It would be neat if we could use a This would be useful for example when you have a GPU but not not enough VRAM to put the model on it. |
👍 Yeah you are right, just didn't think about that when I first made the PR, but I fixed it now, so it's a bit cleaner |
Wanted to also do this, but it's pretty annoying to pass new arguments into the classes from |
Code changes from CompVis/latent-diffusion#123
Allows for running on the CPU if no CUDA device is detected instead of just giving a runtime error.
This should allow for more people to experiment even without owning an nvidia GPU
Solves:
cpu()
#118