# Diffusion in the Cloud In my last post, [Diffusing Diffusion](https://candryan.dev/diffusing-diffusion), I explored AI-generated images on a local machine with [Easy Diffusion](https://github.com/easydiffusion/easydiffusion) (ED). Then I got [AUTOMATIC1111's Stable Diffusion WebUI](https://github.com/AUTOMATIC1111/stable-diffusion-webui) (SD-WebUI) accessible from a Microsoft Azure ML Compute instance, thanks to [Vlad Iliescu's blog](https://vladiliescu.net/stable-diffusion-web-ui-on-azure-ml/). However, I have an improvement to make... > The UI isn't quite as friendly (or powerful) as that by Easy Diffusion, but perhaps they can be swapped...? So, what would it take to run Easy Diffusion in the cloud? SD-WebUI is served through [Gradio](https://gradio.app/), which has a built-in option [for sharing a public URL](https://gradio.app/sharing-your-app/). ED uses [FastAPI](https://fastapi.tiangolo.com/deployment/manually/) and the [Uvicorn ASGI server](https://www.uvicorn.org/) which are lacking such a feature. Enter [ngrok](https://ngrok.com/), which seamlessly handles the port forwarding & security. Let's get started with a new ML Compute instance by first creating a new `compute.yaml` file to describe it. The following will create a compute instance using a `Standard_NC6s_v3` GPU, which shuts downs after 30 minutes of inactivity & at 11 PM each night. ```yaml $schema: https://azuremlschemas.azureedge.net/latest/computeInstance.schema.json type: computeinstance size: Standard_NC6s_v3 idle_time_before_shutdown: "PT30M" schedules: compute_start_stop: - action: stop trigger: type: cron start_time: "2023-06-12T12:00:00" time_zone: UTC expression: 0 23 * * * ``` Execute the following to create the compute instance... ```powershell $resourceGroup = "" $workspace = "" $compute = "" az ml compute create -f compute.yml -n $compute -w $workspace -g $resourceGroup ``` This process takes several minutes, and the status can be checked from [Azure Machine Learning Studio](https://ml.azure.com) or via the following command. ```powershell az ml compute show -n $compute -w $workspace -g $resourceGroup ``` Easy Diffusion has a very simple setup but will require some tweaks in the cloud environment. The main issue is with `conda`, which is installed to the local setup files but, due to file system restrictions will encounter the error: *"cannot copy symlink."* The good news is we don't actually need this `conda.sh` file! First, check if conda is installed `conda --version`. If not already, download via the [Anaconda](https://www.anaconda.com/download#downloads) or [Miniconda](https://docs.conda.io/en/latest/miniconda.html) installer, rather than through `pip` -- *that version is corrupt as a standalone application (*[*ref*](https://github.com/conda/conda/issues/11715)*)*. The faulty `conda` installation must first be ignored by commenting a line of the `bootstrap.sh` file. After the initial setup is complete, the installation can be ignored for subsequent startups by creating a dummy `conda.sh` file -- *this file's existence is checked to determine whether or not* `conda` *should be installed, and is (seemingly) only used by the ED developer console*. From a terminal, in the user home directory... ```bash # Download a release from Github curl https://github.com/easydiffusion/easydiffusion/releases/download/v2.5.24/Easy-Diffusion-Linux.zip --location --output Easy-Diffusion_v2.5.24.zip unzip Easy-Diffusion_v2.5.24.zip cd easy-diffusion/ # Disable internal conda installation nano ./scripts/bootstrap.sh # comment out line 49, which adds 'conda python-3.8.5' to the packages for installation # Install Easy Diffusion (by starting it) ./start.sh # then stop (with CTRL+C), to finish setup # In lieu of actually fixing the problem... mkdir -p installer_files/env/etc/profile.d/ touch installer_files/env/etc/profile.d/conda.sh # Download more models, especially the latest version of stable-diffusion cd models/stable-diffusion/ curl -H "Authorization: Bearer " https://huggingface.co/stabilityai/stable-diffusion-2-1/resolve/main/v2-1_768-ema-pruned.safetensors --location --output v2-1_768-ema-pruned.safetensors curl https://raw.githubusercontent.com/Stability-AI/stablediffusion/main/configs/stable-diffusion/v2-inference-v.yaml --output v2-1_768-ema-pruned.yaml # Navigate back to user home cd ../../../ ``` Now to safely expose the website. While I'm using basic authentication for this example, [ngrok supports more advanced options](https://ngrok.com/blog-post/authentication-with-ngrok). Starting from a terminal in the user home directory... ```bash # Download, make executable, & setup config curl https://bin.equinox.io/c/bNyj1mQVY4c/ngrok-v3-stable-linux-amd64.tgz --location --output ngrok-v3-stable-linux-amd64.tgz tar -xf ngrok-v3-stable-linux-amd64.tgz chmod +x ngrok ./ngrok config add-authtoken ``` At this point, we're ready to roll. Open **two** terminals in the user home directory, and run the following commands: ```bash # Start ngrok with port used by Easy Diffusion & basic authentication ./ngrok http 9000 --basic-auth="USERNAME:PASSWORD" # Start Easy Diffusion cd easy-diffusion/ ./start.sh ``` Just access the URL provided by `ngrok`, and profit 🤑 ![](https://cdn.hashnode.com/res/hashnode/image/upload/v1686513259293/9eef2d22-91b7-48cf-a26a-2a6f0a4cda37.png align="center") I now have a dedicated GPU with 8.5 GB of RAM & the Easy Diffusion UI available from my phone 😎 ![](https://cdn.hashnode.com/res/hashnode/image/upload/v1686513454307/f8d27aa0-c357-484b-9b99-79f1b97913ae.png align="center") And it's so much faster than my personal [NVidia GTX 1050 Ti](https://www.nvidia.com/en-gb/geforce/graphics-cards/geforce-gtx-1050-ti/specifications/)... ![](https://cdn.hashnode.com/res/hashnode/image/upload/v1686513548726/dd378d0b-c36d-4292-b002-9dd9d1fc4538.png align="center") ![](https://cdn.hashnode.com/res/hashnode/image/upload/v1686513555534/f2bca0b8-fe11-426c-a710-469d4eb066c9.png align="center")