stable-diffusion-cpu VS Pytorch

Created with stable-diffusion-cpu on an HP Proliant server, CPU-only, dual Xeon, 16c/32t.

I found this repo early on and have been using it to run inference on my M1 Pro MBP. https://github.com/ModeratePrawn/stable-diffusion-cpu

For me it runs at about 3.5 seconds per iteration per picture at 512x512.

There is also a fork that uses metal here and is much faster: https://github.com/magnusviri/stable-diffusion/tree/apple-si...

Download the code from the Github repo https://github.com/ModeratePrawn/stable-diffusion-cpu and unzip it. Open it on an editor (e.g. VS Code)

For CPU, try: https://github.com/ModeratePrawn/stable-diffusion-cpu

PyTorch (https://pytorch.org/)

torch is a package to manage tensors and dynamic neural networks in python (GitHub)

PyTorch also has some support for them, but it's quite incomplete and has many issues so that it is basically unusable. And its future development is also unclear. https://github.com/pytorch/pytorch/issues/60832

TensorFlow

TensorFlow, developed by Google, and PyTorch, developed by Facebook, are two of the most popular frameworks for building and training complex machine learning models. TensorFlow is known for its flexibility and robust scalability, making it suitable for both research prototypes and production deployments. PyTorch is praised for its ease of use, simplicity, and dynamic computational graph that allows for more intuitive coding of complex AI models. Both frameworks support a wide range of AI models, from simple linear regression to complex deep neural networks.

> does PyTorch have a similar concept

of course https://github.com/pytorch/pytorch/blob/main/torch/utils/_py...

fyi should work on most 40xx[1]

[1] https://github.com/pytorch/pytorch/issues/119638#issuecommen...

Sure, right here: https://github.com/pytorch/pytorch/blob/main/torch/autograd/...

Here's the documentation: https://pytorch.org/tutorials/intermediate/forward_ad_usage....

> When an input, which we call “primal”, is associated with a “direction” tensor, which we call “tangent”, the resultant new tensor object is called a “dual tensor” for its connection to dual numbers[0].

*My post explains Dot, Matrix and Element-wise multiplication in PyTorch.

In PyTorch with @, dot() or matmul():