I’ve spent the last few weeks building a documentary video generation pipeline. A topic goes in (say: “Sankebetsu brown bear incident 1915 Hokkaido”) and a finished mp4 comes out (a ten-minute narrated documentary, 27 scenes, original visuals, voiced by a documentary-grade TTS), all running on a homelab cluster.

Between those two endpoints sits a chain of eight passes that hand off to each other. Several fan out to “do N things in parallel and aggregate.” Several produce artifacts measured in MB per scene that should not be shoved through a message broker. A few are the right place to put a human in the loop, where one minute of editorial review saves hours of GPU time downstream.

Node-based Stable Diffusion UI with AMD MI60 GPU acceleration via ROCm.

Overview#

ComfyUI runs as a containerized service using a custom ROCm 5.7 image built for the MI60’s gfx906 architecture. It can run standalone on both GPUs or alongside vLLM in the image-chat configuration.

Quick Start#

# Build the container image
cd comfyui
make build

# Start ComfyUI (standalone, both GPUs)
make up

# Or use the image-chat config (vLLM on GPU0, ComfyUI on GPU1)
curl -X POST -H "Content-Type: application/json" \
  -d '{"config":"image-chat"}' http://localhost:9100/switch

Access the UI at http://localhost:8188.