---
title: "Image & Video Generation with ComfyUI"
publisher: "nvidia"
type: "playbook"
updated: "2026-05-26T18:38:33.301Z"
description: "Generate images and videos with FLUX, Wan 2.1, HunyuanVideo, and Cosmos on DGX Station"
canonical: "https://build.nvidia.com/station/comfyui.md"
---

# Basic idea

ComfyUI is a node-based visual interface for building image and video generation workflows using 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 complex generation pipelines visually.
- **Multi-model support** covers the latest architectures: FLUX for images, Wan 2.1 and HunyuanVideo for video, and NVIDIA Cosmos for world generation.
- **Full precision on GB300** — with 252 GB of HBM3e, you can run 12–17B image models and 13–14B video models at bf16 with no quantization or offloading, which is impossible on consumer hardware.

# What you'll accomplish

Deploy ComfyUI on DGX Station and run image and video generation workflows using six state-of-the-art models:

- **FLUX.1 [dev]** (12B) — high-quality text-to-image generation
- **HiDream-I1 Full** (17B) — the largest open image model, with four text encoders including Llama-3.1-8B
- **Wan 2.1 T2V/I2V 14B** — text-to-video and image-to-video at 720p
- **HunyuanVideo** (13B) — 1080p video generation leveraging the full GB300 memory (~100–120 GB VRAM)
- **NVIDIA Cosmos-Predict2** (14B) — NVIDIA's world foundation model for video-to-world generation

You will also learn advanced techniques including ControlNet-guided generation and combined image-to-video pipelines.

# What to know before starting

- Basic Docker container usage
- Familiarity with generative AI concepts (prompts, diffusion models) is helpful but not required

# Prerequisites

- NVIDIA DGX Station with GB300 GPU
- Docker installed: `docker --version`
- NVIDIA Container Toolkit configured: `nvidia-smi` should show the GB300
- HuggingFace account with access token: [https://huggingface.co/settings/tokens](https://huggingface.co/settings/tokens)
- At least 200 GB free disk space for model weights
- Network access to HuggingFace and GitHub

# Ancillary files

All required assets can be found in the [ComfyUI playbook repository](https://github.com/NVIDIA/dgx-spark-playbooks/blob/main/nvidia/station-comfyui/).

- `assets/Dockerfile` — Builds the ComfyUI container image from NGC PyTorch base (ARM64)
- `assets/scripts/download-models.sh` — Downloads all model weights from Hugging Face using the **`hf`** CLI (`huggingface-hub` package)
- `assets/workflows/*.json` — Eight **UI** workflows (ComfyUI 0.4 graph with `nodes` / `links`) for **Load** in the web UI
- `assets/workflow_api/*.api.json` — The same eight graphs in **API** format for `/prompt` and automation (`curl`, scripts)
- `assets/scripts/api_to_ui_workflow.py` — Regenerates UI JSON from API JSON if you edit a graph programmatically

# Time & risk

* **Duration:** 45 minutes (excluding model downloads, which may take 30–60 minutes depending on network speed)
* **Risks:**
* Model downloads require HuggingFace authentication and substantial bandwidth (~150 GB total)
* Port 8188 must be accessible for the ComfyUI web interface
* **Rollback:** Stop and remove the Docker container. Delete the `models/` directory to reclaim disk space.
* **Last Updated:** 05/26/2026
* First Publication

## More

- [Instructions](/station/comfyui/instructions.md)
- [Troubleshooting](/station/comfyui/troubleshooting.md)