Skip to main content
Back to Category

Stable Diffusion Setup: Self-Hosted Image Generation

Stable Diffusion installation, AUTOMATIC1111 and ComfyUI interfaces, model downloading, LoRA training, ControlNet usage and Docker self-hosted image generation guide.

Read time: 16 min AI & ML Self-Hosting
stable-diffusionimage generationartificial intelligenceself-hostingcomfyuigpu
Author
REXE Teknoloji Network & Security Team
Editor
REXE Teknoloji Technical Editorial
First published
Last updated

Stable Diffusion Setup: Self-Hosted Image Generation

Stable Diffusion is an open-source AI model that generates images from text. By running it on your own server, you can produce unlimited images, train custom models, and work with complete privacy. This guide covers AUTOMATIC1111 Web UI and ComfyUI installation, model management, LoRA and ControlNet usage in detail.

What is Stable Diffusion?

Stable Diffusion is a latent diffusion model developed by Stability AI. Key features:

  • Text-to-Image (txt2img): Generate images from text descriptions
  • Image-to-Image (img2img): Generate new images using existing images as reference
  • Inpainting: Edit specific areas of an image
  • ControlNet: Controlled generation with pose, edge and depth maps
  • LoRA: Low-resource model fine-tuning
  • Upscaling: Enlarge low-resolution images

System Requirements

ComponentMinimumRecommended
GPUNVIDIA 6 GB VRAMNVIDIA 12 GB+ VRAM
RAM16 GB32 GB+
Disk30 GB100 GB+ (for models)
OSUbuntu 22.04 LTSUbuntu 22.04/24.04 LTS
CUDA11.8+12.1+
Python3.10+3.10.x

AUTOMATIC1111 Web UI Installation

AUTOMATIC1111 is the most popular web interface for Stable Diffusion.

Manual Installation

hljs bash
# Dependencies
sudo apt-get update
sudo apt-get install -y python3-venv python3-pip git wget

# Clone repository
git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
cd stable-diffusion-webui

# Download model (Stable Diffusion XL)
wget -O models/Stable-diffusion/sd_xl_base_1.0.safetensors \
  "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_base_1.0.safetensors"

# Start
./webui.sh --listen --port 7860 --xformers --enable-insecure-extension-access

Docker with AUTOMATIC1111

hljs yaml
version: '3.8'
services:
  stable-diffusion:
    image: ghcr.io/abetlen/stable-diffusion-webui-docker:latest
    container_name: sd-webui
    ports:
      - "7860:7860"
    volumes:
      - sd_models:/app/models
      - sd_outputs:/app/outputs
      - sd_extensions:/app/extensions
    environment:
      - COMMANDLINE_ARGS=--listen --xformers --enable-insecure-extension-access --no-half-vae
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 1
              capabilities: [gpu]
    restart: unless-stopped

volumes:
  sd_models:
  sd_outputs:
  sd_extensions:

Systemd Service

hljs bash
cat > /etc/systemd/system/sd-webui.service << 'EOF'
[Unit]
Description=Stable Diffusion Web UI
After=network.target

[Service]
Type=simple
User=sduser
WorkingDirectory=/opt/stable-diffusion-webui
ExecStart=/opt/stable-diffusion-webui/webui.sh --listen --port 7860 --xformers
Restart=on-failure
RestartSec=10
Environment=HOME=/opt/stable-diffusion-webui

[Install]
WantedBy=multi-user.target
EOF

systemctl daemon-reload
systemctl enable --now sd-webui

ComfyUI Installation

ComfyUI is an advanced node-based Stable Diffusion interface. Ideal for creating complex workflows.

Manual Installation

hljs bash
# Clone repository
git clone https://github.com/comfyanonymous/ComfyUI.git
cd ComfyUI

# Python virtual environment
python3 -m venv venv
source venv/bin/activate

# Dependencies
pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu121
pip install -r requirements.txt

# Copy model to models directory
cp /path/to/sd_xl_base_1.0.safetensors models/checkpoints/

# Start
python main.py --listen 0.0.0.0 --port 8188

Docker with ComfyUI

hljs yaml
version: '3.8'
services:
  comfyui:
    image: ghcr.io/ai-dock/comfyui:latest
    container_name: comfyui
    ports:
      - "8188:8188"
    volumes:
      - comfy_models:/opt/ComfyUI/models
      - comfy_output:/opt/ComfyUI/output
      - comfy_custom:/opt/ComfyUI/custom_nodes
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 1
              capabilities: [gpu]
    restart: unless-stopped

volumes:
  comfy_models:
  comfy_output:
  comfy_custom:

Model Download and Management

Popular Models

hljs bash
# Stable Diffusion XL Base
wget -O models/Stable-diffusion/sd_xl_base_1.0.safetensors \
  "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_base_1.0.safetensors"

# SDXL Refiner
wget -O models/Stable-diffusion/sd_xl_refiner_1.0.safetensors \
  "https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0/resolve/main/sd_xl_refiner_1.0.safetensors"

# Stable Diffusion 1.5 (for low VRAM)
wget -O models/Stable-diffusion/v1-5-pruned-emaonly.safetensors \
  "https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.safetensors"

LoRA Usage

LoRA (Low-Rank Adaptation) allows you to fine-tune large models with low resources.

LoRA Model Download

hljs bash
# Place LoRA models in models/Lora directory
mkdir -p models/Lora

# Download LoRA from Civitai or HuggingFace
wget -O models/Lora/detail-enhancer.safetensors \
  "https://huggingface.co/example/detail-enhancer/resolve/main/model.safetensors"

LoRA Prompt Usage

To use LoRA in AUTOMATIC1111, add it to your prompt:

a beautiful landscape, mountains, sunset <lora:detail-enhancer:0.7>

ControlNet Setup

ControlNet allows you to control image generation with pose, edge maps and depth information.

hljs bash
# ControlNet extension installation (AUTOMATIC1111)
cd extensions
git clone https://github.com/Mikubill/sd-webui-controlnet.git

# ControlNet models
mkdir -p models/ControlNet
wget -O models/ControlNet/control_v11p_sd15_canny.pth \
  "https://huggingface.co/lllyasviel/ControlNet-v1-1/resolve/main/control_v11p_sd15_canny.pth"

wget -O models/ControlNet/control_v11p_sd15_openpose.pth \
  "https://huggingface.co/lllyasviel/ControlNet-v1-1/resolve/main/control_v11p_sd15_openpose.pth"

API Image Generation

Programmatic image generation via AUTOMATIC1111 API:

hljs python
import requests
import base64
from pathlib import Path

def generate_image(prompt, negative_prompt="", steps=30, width=1024, height=1024):
    url = "http://localhost:7860/sdapi/v1/txt2img"
    payload = {
        "prompt": prompt,
        "negative_prompt": negative_prompt,
        "steps": steps,
        "width": width,
        "height": height,
        "sampler_name": "DPM++ 2M Karras",
        "cfg_scale": 7,
        "seed": -1
    }
    
    response = requests.post(url, json=payload)
    result = response.json()
    
    # Save image
    image_data = base64.b64decode(result["images"][0])
    Path("output.png").write_bytes(image_data)
    print("Image saved: output.png")

# Usage
generate_image(
    prompt="a futuristic data center, neon lights, cyberpunk style, 8k",
    negative_prompt="blurry, low quality, distorted"
)

Performance Optimization

hljs bash
# Memory optimization with xformers
pip install xformers

# Launch parameters
./webui.sh --listen \
  --xformers \
  --opt-sdp-attention \
  --no-half-vae \
  --medvram \
  --port 7860

Memory Saving Parameters

ParameterDescriptionVRAM Savings
--xformersxformers attentionMedium
--medvramMedium VRAM modeHigh
--lowvramLow VRAM modeVery high
--opt-sdp-attentionSDP attentionMedium
--no-half-vaeVAE float32Quality improvement

Generate unlimited images with Stable Diffusion on REXE GPU servers. SDXL models run at high speed with NVIDIA A100 GPUs.

Frequently Asked Questions

What is the difference between AUTOMATIC1111 and ComfyUI?

AUTOMATIC1111 offers a user-friendly web interface and is ideal for beginners. ComfyUI provides a node-based interface for creating complex workflows. ComfyUI is preferred for advanced users and automation.

What is the minimum VRAM required for Stable Diffusion?

SD 1.5 models can run with 4 GB VRAM in --lowvram mode. SDXL models require minimum 8 GB VRAM. 12 GB+ VRAM is recommended for comfortable usage. --medvram and --xformers parameters reduce memory usage.

What is LoRA and how is it used?

LoRA (Low-Rank Adaptation) allows you to fine-tune large models to a specific style or subject with low resources. Place LoRA files in the models/Lora directory and use them in prompts with <lora:model_name:weight> format.

What does ControlNet do?

ControlNet allows you to guide image generation with control signals such as pose (OpenPose), edge maps (Canny), depth, and segmentation. This gives you precise control over the composition of generated images.

Can generated images be used commercially?

Stable Diffusion is distributed under the CreativeML Open RAIL-M license. Generated images are generally suitable for commercial use, but it is recommended to check the license of the specific model and LoRA you are using.

How to do batch image generation?

In AUTOMATIC1111, use Batch count and Batch size parameters for bulk generation. You can also send multiple requests via API in a loop. In ComfyUI, create batch workflows with the queue system.

Related Articles

Ollama Installation: Local LLM Running Guide

Ollama installation, running local LLM models (Llama 3, Mistral, Gemma), API usage, model management, GPU acceleration and Docker deployment.

15 min
ollamallmartificial intelligence
Network TrafficInbound— GbpsOutbound— Gbps