Most creators only use the official cloud version of Claude Design with strict rate limits and paid subscriptions. Few people know there is a fully open-source replica of Claude Design that you can deploy locally on your PC for unlimited design generation without network restrictions or billing fees. This tutorial covers complete environment setup, source code deployment, model docking, and common troubleshooting for self-hosted open-source Claude Design, with executable code blocks for every step.
1. Core Advantages of Self-Hosted Open-Source Claude Design
- Unlimited generation: No daily request caps, no token billing for local operation.
- Data privacy: All design drafts, prompts, and image assets are stored locally without uploading to third-party servers.
- Custom model docking: Compatible with local open-source vision models, Ollama, and remote LLM APIs.
- Customizable functions: Modify source code to add brand templates, batch export, and team collaborative modules.
- Low hardware threshold: Runs smoothly on consumer GPUs with 8GB VRAM, even compatible with CPU-only deployment.
2. Pre-Deployment Environment Preparation
2.1 Dependencies Installation
The open-source project relies on Python 3.10+, Git, and Node.js. Run the following installation commands based on your operating system.
Windows (PowerShell Administrator):
# Install winget dependencies
winget install Python.Python.3.10 Git.Git OpenJS.NodeJS.LTS
# Verify installation versions
python --version
git --version
node -v
npm -vmacOS (Terminal):
# Install Homebrew if missing
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
# Install required runtime
brew install python@3.10 git nodeLinux Ubuntu/Debian:
sudo apt update && sudo apt install python3.10 python3-pip git nodejs npm -y2.2 GPU Acceleration Setup (Recommended)
If you have an NVIDIA graphics card, install CUDA toolkit to speed up image rendering:
# Install PyTorch with CUDA support
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu1213. Clone Open-Source Repository & Install Project Dependencies
3.1 Pull Source Code
Create an empty folder and clone the official open-source repo:
# Create working directory
mkdir claude-design-local && cd claude-design-local
# Clone source repository
git clone https://github.com/open-claude-design/claude-design.git
cd claude-design3.2 Backend Python Dependencies Installation
# Create independent virtual environment to avoid version conflicts
python3 -m venv venv
# Activate virtual env (Windows)
venv\Scripts\activate
# Activate virtual env (macOS/Linux)
source venv/bin/activate
# Install all backend requirements
pip install -r requirements.txt3.3 Frontend Web Build
# Enter frontend folder
cd web
# Install npm packages
npm install
# Build static frontend files
npm run build4. Model Configuration: Docking Local & Remote Vision Models
Open-source Claude Design does not embed models by default; you need to configure model access in the config file.
4.1 Modify Core Configuration File
# Open configuration file for editing
cp config.example.yaml config.yamlEdit config.yaml to set model source. Sample Ollama local model configuration:
model_provider: ollama
vision_model: llama3.2-vision:11b
api_base: http://127.0.0.1:11434
api_key: dummy-key
max_image_size: 1024
render_batch: 44.2 Pull Local Vision Model via Ollama
If using Ollama as model backend, run this command to download the vision model before launching the service:
ollama pull llama3.2-vision:11b5. Launch Local Service & Access Web UI
5.1 Start Backend Service
Keep the virtual environment activated and execute:
# Launch backend API service
python main.py --host 0.0.0.0 --port 78905.2 Access Design Panel
Open your browser and visit http://127.0.0.1:7890 — the fully functional open-source Claude Design interface will load. You can input UI design prompts, upload reference sketches, generate page layouts, and export PNG/SVG design files locally without any network limits.
Sample practical design prompt you can directly input in the local tool:
Design a minimalist AI tool landing page, mobile-first layout, blue tech tone, include feature card section, download button and footer copyright. Output clean Figma-style layered design.6. Common Beginner Pitfalls & Fix Commands
Pitfall 1: Port 7890 Occupied, Service Startup Failed
Check occupied port and kill process:
# Windows
netstat -ano | findstr "7890"
taskkill /F /PID [target-PID-number]# macOS/Linux
lsof -i :7890
kill -9 [process-id]Pitfall 2: Image Generation Black Screen, Model Connection Error
Restart Ollama and reload service:
ollama stop llama3.2-vision:11b
ollama start
# Rerun backend
python main.pyPitfall 3: Frontend Page Blank After npm Build
Reinstall frontend dependencies:
cd web
rm -rf node_modules package-lock.json
npm install && npm run build7. Advanced Optimization for Long-Term Use
- Auto-start service on system boot: Write a simple startup script to run backend automatically after PC startup.
- Batch export script: Add a Python script to mass export all generated design drafts to a local folder:
# Batch export all design drafts example
import os
import shutil
export_dir = "./export_designs"
source_dir = "./static/output"
os.makedirs(export_dir, exist_ok=True)
for file in os.listdir(source_dir):
if file.endswith((".png", ".svg")):
shutil.copy2(os.path.join(source_dir, file), export_dir)
print("All design files exported successfully")- LAN sharing access: Modify launch command to allow other devices on the same Wi-Fi to visit your design tool:
python main.py --host 0.0.0.0 --port 7890Other devices access via http://[your-local-ip]:7890.
FAQ
Does the open-source Claude Design require a powerful GPU?
No. While a GPU with 8GB+ VRAM is recommended for optimal performance, the project supports CPU-only deployment. With CPU mode, generation is slower but fully functional. Consumer GPUs like RTX 3060 or even laptop GPUs work well for most design tasks. You can also offload model inference to a remote Ollama server on your LAN if your local machine lacks GPU power.
Is the open-source version feature-complete compared to the official Claude Design?
The open-source replica covers the core design generation workflow — prompt-to-UI, reference sketch upload, and design export (PNG/SVG). However, the official Claude Design cloud version offers additional features like collaborative editing, version history, and premium design templates. For solo creators and small teams who primarily need AI-powered UI generation, the open-source version is more than sufficient.
Can I customize the generated UI designs with my own brand styles?
Yes. Since the source code is fully open, you can modify the prompt templates, color palettes, and layout presets in the config files. You can also add custom brand assets (logos, fonts, color schemes) to the static assets folder, and the generation pipeline will incorporate them. Advanced users can fine-tune the underlying vision model on their own design dataset for brand-specific outputs.