Generate images and videos with FLUX, Wan 2.1, HunyuanVideo, and Cosmos on DGX Station
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.
Deploy ComfyUI on DGX Station and run image and video generation workflows using six state-of-the-art models:
You will also learn advanced techniques including ControlNet-guided generation and combined image-to-video pipelines.
docker --versionnvidia-smi should show the GB300All required assets can be found in the ComfyUI playbook repository.
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 UIassets/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 programmaticallymodels/ directory to reclaim disk space.comfyui-gb300, ~24 GB), container starts and serves on port 8188, all 8 mounted UI workflows enumerate correctly, /object_info returns 1092 node types, /prompt validation rejects on missing-model with clean errors. Documented benign startup warnings (aimdo CUDA-hook fallback, urllib3 / charset_normalizer version skew) so users do not chase non-issues.ae_hidream.safetensors, HunyuanVideo CLIP → clip_l_hunyuan.safetensors), --gpus device=0 default, df -h / prereq, ~/.local/bin PATH guidance, FLUX node list aligned with the actual graph, .webp output (not MP4), HF token via env not CLI, container output chown cleanup hint.