What does ComfyUI actually do?
A lot of visual AI tools work well when the job is simple: type a prompt, pick a style, wait for an image. They start to break down when you need something more repeatable, like the same generation chain across multiple campaigns, a custom LoRA in the middle of the process, or a video workflow where you need to inspect why one step failed. Instead of hiding that pipeline, ComfyUI lays it out on a node canvas so you can see where the model loads, where conditioning changes, where upscaling happens, and where outputs get written. For people doing serious image or video iteration, that level of visibility matters because debugging a workflow is often more important than generating a single impressive sample.
ComfyUI handles that by treating generation as a graph you can edit, save, and rerun. The homepage, docs, and GitHub README all point to the same pattern: connect models, processing steps, and outputs, then start from community workflows if you do not want to begin on a blank canvas. The product also stretches beyond local experimentation. You can run it on your own hardware, use the hosted cloud version with pre-installed models and custom node support, or expose a finished workflow through the API once the graph is stable. That combination is what makes ComfyUI more than a hobbyist node editor. It can sit at the messy beginning of experimentation and still remain useful when a team wants something repeatable enough for production use.