Basic idea
ComfyUI is an open-source, node-based web application for AI image and video generation with diffusion models. Instead of a single text box, you connect processing nodes — model loaders, text encoders, samplers, decoders — into a graph that gives full control over every generation step.
- Node-based workflows let you build, modify, and share generation pipelines visually. Workflows save as JSON for versioning, collaboration, and reproducibility.
- Multi-model support covers Stable Diffusion, FLUX for images, Wan 2.1 and HunyuanVideo for video, and NVIDIA Cosmos for world generation.
- GPU-accelerated inference runs on your hardware platform so generation stays local.
What you'll accomplish
Install and run ComfyUI on your hardware platform, then generate images (and optionally video) from a browser UI on port 8188.
Start with the Image Gen Quick Start tab for a lighter host Python install with Stable Diffusion 1.5. Use the Video Gen Workflow tab for container-based FLUX / Wan / HunyuanVideo / Cosmos / ControlNet workflows.
What to know before starting
Required:
- Basic command-line and terminal usage
- Familiarity with Python virtual environments or Docker containers (depending on install path)
Optional:
- Familiarity with generative AI concepts (prompts, diffusion models, checkpoints)
- Hugging Face account for gated model downloads (container / large-model path)
Supported hardware platforms
Use the matrix below to confirm your hardware platform, OS, memory, and whether multi-node applies.
| Hardware platform | OS | Memory | Multi-node capable hardware |
|---|
| DGX Spark | DGX OS (Linux) | 128 GB Unified Memory | — |
| DGX Station | DGX OS (Linux) | Large HBM + Grace DRAM | — |
Prerequisites
Hardware requirements
- Supported hardware platform — see Supported hardware platforms matrix above
- Image Gen Quick Start (host Python): at least ~20 GB free disk; ~8 GB GPU memory for Stable Diffusion–class models
- Video Gen Workflow (container + tiered models): at least ~70 GB free disk for Tier 1 (~230 GB for all tiers); peak GPU memory about ~80 GB / ~100 GB / ~120 GB for Tiers 1–3 (see that tab)
Software requirements
- Image Gen Quick Start: Python 3.8+, pip, Git, CUDA toolkit compatible with your GPU, network access to Hugging Face
- Video Gen Workflow: Docker, NVIDIA Container Toolkit, Hugging Face access token, network access to NGC / Hugging Face / GitHub
- Web browser access to port
8188 on the hardware platform
Ancillary files
Playbook assets (Video Gen Workflow) live under assets/ in this repository:
assets/Dockerfile — Builds the ComfyUI container image from an NGC PyTorch baseassets/scripts/download-models.sh — Downloads model weights from Hugging Face (hf CLI)assets/workflows/*.json — UI workflows for Load in the web UIassets/workflow_api/*.api.json — Same graphs in API format for /prompt and automationassets/scripts/api_to_ui_workflow.py — Regenerates UI JSON from API JSON if you edit a graph programmatically
For Image Gen Quick Start, clone ComfyUI on GitHub directly (requirements.txt, main.py, and checkpoint directories).
Time & risk
- Estimated time: 45 MIN (longer on first run when downloading large models)
- Risk level: Medium
- Model downloads are large and may fail due to network or auth issues
- Port 8188 must be reachable for the web UI
- Rollback: Remove the virtual environment and clone (Image Gen Quick Start), or stop/remove the container and optionally delete
models/ (Video Gen Workflow) — non-destructive to the host OS
- Last Updated: 07/27/2026
- Image Gen Quick Start and Video Gen Workflow tabs; start with image gen, then scale to tiered video models
