6.7 KiB
Comfy UI
Install and use ComfyUI to generate images
Table of Contents
Overview
Basic idea
ComfyUI is an open-source web server application for AI image generation using diffusion-based models like SDXL, Flux and others. It has a browser-based UI that lets you create, edit and run image generation and editing workflows with multiple steps. Generation and editing steps (e.g. loading a model, adding text or sampling) are configurable in the UI as a node, and you connect nodes with wires to form a workflow.
ComfyUI uses the host's GPU for inference, so you can install it on your Spark and do all of your image generation and editing directly on device.
Workflows are saved as JSON files, so you can version them for future work, collaboration and reproducibility.
What you'll accomplish
You'll install and configure ComfyUI on your NVIDIA DGX Spark device so you can use the unified memory to work with large models.
What to know before starting
- Experience working with Python virtual environments and package management
- Familiarity with command line operations and terminal usage
- Basic understanding of deep learning model deployment and checkpoints
- Knowledge of container workflows and GPU acceleration concepts
- Understanding of network configuration for accessing web services
Prerequisites
Hardware Requirements:
- NVIDIA Spark device with Blackwell architecture
- Minimum 8GB GPU memory for Stable Diffusion models
- At least 20GB available storage space
Software Requirements:
- Python 3.8 or higher installed:
python3 --version - pip package manager available:
pip3 --version - CUDA toolkit compatible with Blackwell:
nvcc --version - Git version control:
git --version - Network access to download models from Hugging Face
- Web browser access to
<SPARK_IP>:8188port
Ancillary files
All required assets can be found in the ComfyUI repository on GitHub
requirements.txt- Python dependencies for ComfyUI installationmain.py- Primary ComfyUI server application entry pointv1-5-pruned-emaonly-fp16.safetensors- Stable Diffusion 1.5 checkpoint model
Time & risk
- Estimated time: 30-45 minutes (including model download)
- Risk level: Medium
- Model downloads are large (~2GB) and may fail due to network issues
- Port 8188 must be accessible for web interface functionality
- Rollback: Virtual environment can be deleted to remove all installed packages. Downloaded models can be removed manually from the checkpoints directory.
Instructions
Step 1. Verify system prerequisites
Check that your NVIDIA Spark device meets the requirements before proceeding with installation.
python3 --version
pip3 --version
nvcc --version
nvidia-smi
Expected output should show Python 3.8+, pip available, CUDA toolkit and GPU detection.
Step 2. Create Python virtual environment
You will install ComfyUI on your host system, so you should create an isolated environment to avoid conflicts with system packages.
python3 -m venv comfyui-env
source comfyui-env/bin/activate
Verify the virtual environment is active by checking the command prompt shows (comfyui-env).
Step 3. Install PyTorch with CUDA support
Install PyTorch with CUDA 12.9 support.
pip3 install torch torchvision --index-url https://download.pytorch.org/whl/cu129
This installation targets CUDA 12.9 compatibility with Blackwell architecture GPUs.
Step 4. Clone ComfyUI repository
Download the ComfyUI source code from the official repository.
git clone https://github.com/comfyanonymous/ComfyUI.git
cd ComfyUI/
Step 5. Install ComfyUI dependencies
Install the required Python packages for ComfyUI operation.
pip install -r requirements.txt
This installs all necessary dependencies including web interface components and model handling libraries.
Step 6. Download Stable Diffusion checkpoint
Navigate to the checkpoints directory and download the Stable Diffusion 1.5 model.
cd models/checkpoints/
wget https://huggingface.co/Comfy-Org/stable-diffusion-v1-5-archive/resolve/main/v1-5-pruned-emaonly-fp16.safetensors
cd ../../
The download will be approximately 2GB and may take several minutes depending on network speed.
Step 7. Launch ComfyUI server
Start the ComfyUI web server with network access enabled.
python main.py --listen 0.0.0.0
The server will bind to all network interfaces on port 8188, making it accessible from other devices.
Step 8. Validate installation
Check that ComfyUI is running correctly and accessible via web browser.
curl -I http://localhost:8188
Expected output should show HTTP 200 response indicating the web server is operational.
Open a web browser and navigate to http://<SPARK_IP>:8188 where <SPARK_IP> is your device's IP address.
Step 9. Optional - Cleanup and rollback
If you need to remove the installation completely, follow these steps:
Warning: This will delete all installed packages and downloaded models.
deactivate
rm -rf comfyui-env/
rm -rf ComfyUI/
To rollback during installation, press Ctrl+C to stop the server and remove the virtual environment.
Step 10. Optional - Next steps
Test the installation with a basic image generation workflow:
- Access the web interface at
http://<SPARK_IP>:8188 - Load the default workflow (should appear automatically)
- Click "Run" to generate your first image
- Monitor GPU usage with
nvidia-smiin a separate terminal
The image generation should complete within 30-60 seconds depending on your hardware configuration.
Troubleshooting
| Symptom | Cause | Fix |
|---|---|---|
| PyTorch CUDA not available | Incorrect CUDA version or missing drivers | Verify nvcc --version matches cu129, reinstall PyTorch |
| Model download fails | Network connectivity or storage space | Check internet connection, verify 20GB+ available space |
| Web interface inaccessible | Firewall blocking port 8188 | Configure firewall to allow port 8188, check IP address |
| Out of GPU memory errors after manually flushing buffer cache | Insufficient VRAM for model | Use smaller models or enable CPU fallback mode |
Note: DGX Spark uses a Unified Memory Architecture (UMA), which enables dynamic memory sharing between the GPU and CPU. With many applications still updating to take advantage of UMA, you may encounter memory issues even when within the memory capacity of DGX Spark. If that happens, manually flush the buffer cache with:
sudo sh -c 'sync; echo 3 > /proc/sys/vm/drop_caches'