How to Deploy Stable Diffusion on AWS: The Brutally Honest, No-Fluff Guide

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Why Even Host Stable Diffusion Yourself?

Before you drop $0.89/hour on an EC2 instance, ask yourself:

• Are you generating NSFW or private content?
• Are you building a creative tool and don’t want to be throttled by APIs?
• Are you just doing this because local GPUs are mythical creatures and Google Colab keeps kicking you off?

If yes to any of the above, AWS can work. But buckle up. This is not plug-and-play.

What You Need to Know First

Forget the tutorials written by engineers who haven’t touched AWS in months. Here’s what matters:

• GPU Type: You want a g4dn.xlarge (cheap but slow) or g5.xlarge (faster but pricier). Don’t overthink it yet.
• AMI (Amazon Machine Image): Start with Deep Learning AMI (Ubuntu) — it comes with drivers and CUDA pre-installed. Saves you hours of pain.
• Storage: Go with at least 30 GB. Models and weights are big boys.
• Region: Use us-east-1 unless you love broken availability and paying more for no reason.

Deployment

Step 1: Launch the Instance

• EC2 > Launch Instance
• Choose Deep Learning AMI (Ubuntu 20.04).
• Choose g5.xlarge or g4dn.xlarge.
• Add 30–50 GB storage.
• Enable SSH and HTTP/HTTPS in your security group.
• Hit “Launch” and grab your key pair.

Step 2: SSH In and Set Up

Ssh -i your-key.pem ubuntu@your-public-ip

Step 3: Clone a Working Repo

Most “Stable Diffusion UI” projects are bloated or outdated. These are solid:

• automatic1111 (the classic OG with every feature imaginable)
• InvokeAI (more production-y and sleek)

Step 4: Install Requirements

Even with the Deep Learning AMI, you’ll probably need

sudo apt update
sudo apt install git -y
git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
cd stable-diffusion-webui
bash webui.sh

It’ll download weights, install dependencies, and maybe crash once. Just re-run it.

Model Weights & Where to Get Them

• SD 1.5: Great starter, fewer VRAM requirements.
• SDXL: New hotness. Needs more VRAM (10GB+) and longer gen times.
• Custom models: Go to CivitAI and fall into the rabbit hole.

Pro tip:

cd models/Stable-diffusion/
wget https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/blob/main/sd-v1-4.ckpt

Just make sure to point the app to the .ckpt or .safetensors file in the web UI.

Expose the Web UI

By default, it runs on localhost:7860. That’s no use unless you love port forwarding.

The Hidden Costs You’ll Forget

• EBS Storage: You get billed even if the instance is stopped.
• Data Transfer: Over 1GB outbound traffic? You’re getting charged.
• Idle Instances: $0.89/hour means ~$640/month — don’t leave it on.

Use an instance to stop or terminate it when not in use. Better yet, set up a shell script to kill it if idle for X minutes.

Gotchas That’ll Ruin Your Night

• CUDA version mismatch: Suddenly torch.cuda.is_available() returns False. Check your NVIDIA driver. Or just use the Deep Learning AMI and avoid this hell.
• Out of VRAM: SDXL + hires fix + 512x768 = boom. Try lower resolution or the — medvram flag.
• Random crashes after 20 gens: Check the temp folder. Logs. RAM usage. Or just restart the instance. Classic “it works when I reboot” syndrome.

Pro-Level Tips You Won’t Find in the Docs

• Use Cloudflare Tunnel if you want secure external access without IP madness.
• Keep models in S3 and symlink them if you’re switching often.
• Enable auto-termination after idle — there are Lambda scripts for that.
• Always snapshot your working instance before updating anything.
• Try Tailscale to turn your EC2 into a VPN-accessible node.

Self-hosting Stable Diffusion on AWS is like building your pizza oven. Sure, it’s hot, dangerous, and totally overkill — but also deeply satisfying.