标签：#comfyui

Comfy MCP is Comfy's first-party local Model Context Protocol server. It lets an MCP-capable coding agent inspect the models and nodes in your ComfyUI installation, validate workflows, run them, and retrieve the outputs. The detail that prevents the most confusion is that two processes are involved : comfy launch starts ComfyUI. Your AI client starts comfy-mcp as a local stdio server. If you run comfy-mcp directly and it appears to do nothing, it is probably waiting for an MCP client. That is normal for a stdio server. Disclosure and verification scope: AI tools assisted with drafting and editing this adaptation. I reviewed the finished article and checked the commands and material claims against Comfy's official documentation, repository, and PyPI pages on 13 August 2026. I have not run a generation on my own hardware for this article, so this is a documentation-verified setup guide, not a hands-on performance test. Comfy's documentation currently labels the MCP offering a public beta, so tools and behaviour may change. What you need Before starting, have: Python 3.10 or newer. The examples below use Python 3.11. comfy-cli 1.14.0 or newer. A ComfyUI workspace, either created with comfy install or selected with comfy set-default . An MCP client that can start a local stdio server, such as Claude Code, Cursor, or Claude Desktop. The models and custom nodes required by the workflow you want to run. The MCP bridge is not what determines the hardware requirement; the selected ComfyUI workflow does. A small image workflow and a large video workflow can have very different memory needs. 1. Install comfy-cli and comfy-mcp I prefer a dedicated virtual environment. It keeps the executables in a predictable place and avoids mixing these packages with unrelated Python projects. Windows PowerShell mkdir comfy-mcp-guide cd comfy-mcp-guide py -3 . 11 -m venv . venv . \.venv\Scripts\Activate.ps1 python -m pip install --upgrade pip python -m pip install "comfy-cli>=1.14.0" comfy-mc

Turning entire novels into narrated, lip-synced, motion video — locally, on a single 16GB GPU, with FLUX, Wan2.2, PuLID, MuseTalk, and ComfyUI. There's an old line: a picture is worth a thousand words. I'd extend it — a video is worth a thousand pictures. So I spent the last few months testing that idea the hard way: I built a pipeline that turns an entire novel into a narrated, lip-synced, motion video. Feed it Pride and Prejudice , or a 400-year-old tale from Strange Tales from a Chinese Studio (聊斋志异), and out comes a finished film with consistent character faces across a hundred-plus scenes. One person. One RTX 4060Ti with 16GB of VRAM. Everything local, no cloud inference. I called it iTube. And honestly — the videos came out better than I expected. Watching a novel you know become a moving, voiced, paced thing is a genuinely different experience from reading a summary of it. That's the bet: that video is a better medium for conveying a story than text is, and the finished clips have mostly convinced me the bet was right. See for yourself before reading another word about how it works: 🎬 Pride and Prejudice — a Western public-domain classic 🎬 聊斋志异 / Strange Tales from a Chinese Studio — a playlist of classical Chinese tales But this post isn't a demo reel. It's about the part nobody warns you about — the part between the models — because that's where the real work turned out to be. The models are instruments, not magic boxes Here's the single most important thing I learned, and the thing I'd want any collaborator to understand before touching a pipeline like this: Each AI package can do exactly one thing well, and your entire job is knowing precisely where each one's ability ends — then writing your script logic to route around the gaps. The stack is not exotic. Most people in generative media know these pieces: FLUX generates the still image for a scene. That's all it does — a single frame. Beautiful, controllable with the right prompt, but frozen. Wan2.2 takes