Run a Stable Diffusion Image Generator Locally on Raspberry Pi 5
Create stunning AI-generated images on your Pi — without internet!
️ Overview
Stable Diffusion is a powerful open-source image generation model that turns text prompts into photorealistic or artistic images. While it's typically run on desktops with powerful GPUs, recent optimizations and the Raspberry Pi 5's performance improvements make it possible (with compromises) to run lightweight versions of it locally.
In this article, we’ll show you how to:
✅ Run a CPU-friendly version of Stable Diffusion on Raspberry Pi 5
✅ Generate images from text prompts
✅ Use Python and diffusers library from HuggingFace
✅ Work fully offline after setup
⚠️ This guide uses a highly optimized model suited for CPU-only inference. Don’t expect real-time speed — but it works!
Requirements
| Component | Version / Notes |
|---|
| Raspberry Pi | Pi 5 (8GB RAM strongly recommended) |
| OS | Raspberry Pi OS Bookworm (64-bit) |
| Python | ≥ 3.10 |
| Disk Space | ~6 GB |
| Internet | Only for installation and model download |
| Model | Stable Diffusion 1.5 (CPU-optimized) |
Step 1: System Setup
sudo apt update && sudo apt upgrade -y
sudo apt install python3 python3-venv python3-pip -y
Create and activate a Python virtual environment:
python3 -m venv sd-env
source sd-env/bin/activate
Step 2: Install Required Python Libraries
pip install --upgrade pip
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
pip install diffusers transformers accelerate scipy safetensors
We use the CPU-only version of PyTorch to ensure compatibility with the Raspberry Pi’s hardware.
Step 3: Download a CPU-optimized Model
Let’s use the runwayml/stable-diffusion-v1-5 or smaller variant from HuggingFace.
You can use this script to automatically load and save the model locally:
from diffusers import StableDiffusionPipeline
import torch
pipeline = StableDiffusionPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5",
torch_dtype=torch.float32,
)
pipeline = pipeline.to("cpu")
prompt = "A cyberpunk robot cat, neon background"
image = pipeline(prompt).images[0]
image.save("output.png")
Tip: Use Smaller Models
If your Pi struggles, try:
from diffusers import DiffusionPipeline
pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base")
Or explore CPU-optimized models like:
CompVis/stable-diffusion-v1-4
Linaqruf/stable-diffusion-1-5-better-vae
SG161222/Realistic_Vision_V5.1_noVAE
How Long Does It Take?
On Raspberry Pi 5 (8GB):
| Image Size | Inference Time |
|---|
| 256x256 | ~2-4 minutes |
| 512x512 | ~7-12 minutes |
⚠️ Do not expect desktop GPU speeds — but for low-volume, offline creative projects, it's functional.
Optional: Serve as a Web App with Gradio
pip install gradio
Add this to the script:
import gradio as gr
def generate(prompt):
image = pipeline(prompt).images[0]
return image
gr.Interface(fn=generate, inputs="text", outputs="image").launch()
Access locally via http://localhost:7860
Example Output
| Prompt | Result |
|---|
| “A medieval knight riding a dragon through the clouds” | ️  |
| “A serene lake in the forest during sunset, ultra-realistic” | ️ |
(You can host real examples or dummy images later on your blog)
Summary
You’ve now turned your Raspberry Pi 5 into a local AI image generator using Stable Diffusion. While it’s not lightning-fast, it’s fully offline, works with open-source tools, and gives you creative freedom at the edge.
