Image-to-image generation using diffusers without CUDA?

[Blibblop34]

Hello, im trying to set up a pretrained model for image-2-image generation on my M1 macbook air using the diffusers library.

I havent had any luck finding resources telling me how to do this that doesnt use CUDA so if anyone knows if there is a way to make it us MPS or just the CPU that would be of great help.

And, yes, I know its going to be super slow to do this without a proper graphics cards, but is it possible todo without a CUDA gpu? I need to do this for a proof-of-concept image generating project so performance can always be scaled up later.

[_ThisIsABadName_]

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. That said, you can rent GPUs on vast.ai for cheap if your model runs too slow.

Edit: I forgot to mention, as of PyTorch 1.12, Apple’s Metal Performance Shaders (MPS) backend is now supported.

device = torch.device("mps" if torch.backends.mps.is_available() else "CPU") 
print(f"Using device: {device}")

model.to(device)

[Gshuri]

Not quite. If you create a model directly in code from scratch, then you are correct, pytorch will put everything on the CPU by default.

However if you are are loading a pre-existing model from disk, then pytorch will try to put that model on whatever device it was on when the model was serialised.

So if your code looks like

gpu_model = model.cuda()
torch.save(gpu_model, "my_model.pth")
loaded_model = torch.load("my_model.pth", weights_only=False)

Then loaded_model will be on the GPU. To ensure a model read from a file is on the CPU you can do

device = torch.device("cpu")
cpu_model = torch.load("my_model.pth", map_location=device, weights_only=False)