Your own Stable Diffusion endpoint with AWS SageMaker

• aws-sagemaker-stable-diffusion

  1 35 0.0 Python

  How to use stable diffusion model on AWS Sagemaker

In the aws-sagemaker-stable-diffusion repo you will find everything needed in order to spin-up your own personal public endpoint with a Stable Diffusion model deployed using AWS SageMaker to show your friends 😎

• pyenv-installer

  17 3,860 2.9 Shell

  This tool is used to install `pyenv` and friends.

  

For python, it's recommended to use pyenv, which allows you to install several versions of python at the same time with simple commands like this: pyenv install 3.9.13

• WorkOS

  workos.com sponsored

  The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.

  

• nvidia-docker

  53 16,998 0.0 Makefile

  Discontinued Build and run Docker containers leveraging NVIDIA GPUs

  

For my local computer, I used nvidia-docker to allow myself to run the code inside a container. This way I don't have to worry about installing the right version of CUDA (relative to pytorch) on my machine.

• sagemaker-huggingface-inference-toolkit

  1 208 7.5 Python
  

With that, we should have the what we need in the bucket. Now, let's create the SageMaker endpoint. To manage the endpoint, as stated before, we use the SageMaker toolkit for huggingface. As such, we have three python scripts that serve as functions to create, use and delete the endpoint.

• diffusers

  266 22,543 9.9 Python

  🤗 Diffusers: State-of-the-art diffusion models for image and audio generation in PyTorch and FLAX.

  

However, since we will use diffusion type of model, and not transformers, the default inference code will need to be overwritten to use the diffusers package (also from huggingface), just like we do in the local/code.py

• notebooks

  17 3,277 8.4 Jupyter Notebook

  Notebooks using the Hugging Face libraries 🤗 (by huggingface)

  

In order to overwrite it, the package readme has some general information about it, and also there is an example in this jupyter notebook. We are doing what is necessary via the files inside sagemaker/code, which has the inference code following SageMaker requirements, and a requirements.txt, that has the necessary dependencies that will be installed when the endpoint gets created

• git-lfs

  159 12,443 9.1 Go

  Git extension for versioning large files

  

To be able to clone the repo with the ML model (stable-diffusion-v1-4/), we will also need git-lfs (that allows versioning of large files). The model is defined as a submodule of this repo and after attempting to clone the submodule, you will be requested to provide your huggingface account credentials in order to clone it. This it to confirm that you have accepted the license required to have access to the model weights.

• InfluxDB

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