Stable Diffusion WebUI
Web interface for Stable Diffusion image generation.
Overview
Stable Diffusion is a revolutionary open-source deep learning model that generates high-quality images from text descriptions using latent diffusion techniques. Released in 2022 by Stability AI, it democratized AI image generation by running efficiently on consumer hardware, unlike previous models that required expensive cloud services. The AUTOMATIC1111 WebUI has become the de facto standard interface for Stable Diffusion, providing an intuitive web-based platform with advanced features like inpainting, outpainting, and ControlNet integration.
This Docker configuration deploys the AUTOMATIC1111 Stable Diffusion WebUI with full GPU acceleration support, enabling users to generate stunning artwork, concept designs, and creative imagery through a browser interface. The WebUI includes sophisticated sampling methods, prompt engineering tools, and extension support that transforms basic text-to-image generation into a comprehensive creative suite. The containerized approach eliminates complex Python environment management while providing consistent performance across different systems.
This stack is ideal for digital artists, game developers, content creators, and AI enthusiasts who want professional-grade image generation capabilities without cloud dependencies. The WebUI's extensive customization options, model management system, and active community ecosystem make it perfect for both hobbyists exploring AI art and professionals integrating generative AI into production workflows.
Key Features
- Text-to-image generation with 50+ sampling algorithms including DPM++, Euler, and DDIM
- Image-to-image transformation with strength controls and noise injection
- Inpainting and outpainting for precise image editing and extension
- LoRA (Low-Rank Adaptation) support for style fine-tuning and character consistency
- Textual Inversion embeddings for custom concepts and artistic styles
- ControlNet integration for pose, depth, and edge-guided image generation
- Extensions marketplace with 200+ community plugins for advanced functionality
- Batch processing and grid generation for exploring parameter variations
Common Use Cases
- 1Digital art studios generating concept art and character designs for games and films
- 2Marketing agencies creating unique visuals for campaigns and social media content
- 3Independent artists exploring AI-assisted creative workflows and style experimentation
- 4Game developers prototyping environments, textures, and asset concepts
- 5E-commerce businesses generating product mockups and lifestyle imagery
- 6Educational institutions teaching AI art generation and machine learning concepts
- 7Content creators producing thumbnails, illustrations, and visual storytelling elements
Prerequisites
- NVIDIA GPU with 8GB+ VRAM (RTX 3070 or better recommended for optimal performance)
- Docker and Docker Compose installed with NVIDIA Container Toolkit configured
- 50GB+ available disk space for models, outputs, and extensions
- 16GB+ system RAM for stable operation during high-resolution generation
- Understanding of Stable Diffusion concepts: prompts, samplers, and CFG scale
- Basic familiarity with model formats (.safetensors, .ckpt) and installation procedures
For development & testing. Review security settings, change default credentials, and test thoroughly before production use. See Terms
docker-compose.yml
docker-compose.yml
1services: 2 stable-diffusion: 3 image: ghcr.io/geszti/sd-webui:latest4 container_name: stable-diffusion5 restart: unless-stopped6 volumes: 7 - sd_models:/app/stable-diffusion-webui/models8 - sd_outputs:/app/stable-diffusion-webui/outputs9 ports: 10 - "7860:7860"11 deploy: 12 resources: 13 reservations: 14 devices: 15 - driver: nvidia16 count: all17 capabilities: [gpu]1819volumes: 20 sd_models: 21 sd_outputs: .env Template
.env
1# Requires NVIDIA GPU with 8GB+ VRAMUsage Notes
- 1Docs: https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki
- 2Access at http://localhost:7860 - first start downloads model (~4GB)
- 3Place .safetensors models in /models/Stable-diffusion folder
- 4LoRAs go in /models/Lora, embeddings in /embeddings
- 5Requires 8GB+ VRAM, use --medvram or --lowvram for less
- 6Extensions marketplace available in UI for extra features
Quick Start
terminal
1# 1. Create the compose file2cat > docker-compose.yml << 'EOF'3services:4 stable-diffusion:5 image: ghcr.io/geszti/sd-webui:latest6 container_name: stable-diffusion7 restart: unless-stopped8 volumes:9 - sd_models:/app/stable-diffusion-webui/models10 - sd_outputs:/app/stable-diffusion-webui/outputs11 ports:12 - "7860:7860"13 deploy:14 resources:15 reservations:16 devices:17 - driver: nvidia18 count: all19 capabilities: [gpu]2021volumes:22 sd_models:23 sd_outputs:24EOF2526# 2. Create the .env file27cat > .env << 'EOF'28# Requires NVIDIA GPU with 8GB+ VRAM29EOF3031# 3. Start the services32docker compose up -d3334# 4. View logs35docker compose logs -fOne-Liner
Run this command to download and set up the recipe in one step:
terminal
1curl -fsSL https://docker.recipes/api/recipes/stable-diffusion-webui/run | bashTroubleshooting
- CUDA out of memory errors: Add --medvram or --lowvram arguments to container startup command
- Model download failures: Manually download models to the sd_models volume and restart container
- WebUI not accessible on port 7860: Check firewall settings and ensure port mapping is correct
- Extensions failing to install: Clear extension cache and ensure stable internet connection
- Generated images appear corrupted: Update to latest WebUI version and verify model integrity
- Slow generation times: Enable xformers optimization and adjust batch size settings
Community Notes
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