winnougan/comfyui-lmstudio — explained in plain English
Analysis updated 2026-05-18
Caption a batch of images locally to build a LoRA training dataset without cloud API costs.
Add a local text generation step into an existing ComfyUI image workflow.
Swap ComfyUI's built-in CLIPLoader and TextGenerate nodes for a local LM Studio backend.
Experiment with vision-capable local models for automatic image description.
| winnougan/comfyui-lmstudio | 0-bingwu-0/live-interpreter | 010zx00x1/faresnipe | |
|---|---|---|---|
| Stars | 2 | 2 | 2 |
| Language | Python | Python | Python |
| Setup difficulty | moderate | moderate | easy |
| Complexity | 3/5 | 2/5 | 2/5 |
| Audience | developer | general | general |
Figures from each repo's GitHub metadata at analysis time.
Requires LM Studio already running locally with a loaded model and its Developer server started.
This project connects ComfyUI, a visual tool for building AI image generation workflows, to LM Studio, an app that runs AI language and vision models directly on your own computer. It adds two building blocks you can drag into a ComfyUI workflow so you can use a locally running model for tasks like writing image captions or generating text, without sending anything to a cloud service or needing an API key. The first block connects to your running LM Studio server, checking right away that the server is reachable and that the model name you typed matches what is actually loaded, so you get a clear error immediately rather than a failure partway through a batch. The second block sends the actual request: you give it a prompt, and optionally an image if you want the model to describe or caption that picture, along with the usual controls like how creative the response should be. The result comes back as plain text that can be saved to a file or shown directly inside your workflow. The README spends real attention on one specific gotcha: some models write out a long hidden reasoning process before giving their final answer, and if you set the response length limit too low, the model runs out of room mid-thought and you get messy, half-finished text instead of a clean caption. It gives concrete guidance, suggesting a much higher limit for these reasoning-style models than for ordinary ones. Setup involves dropping the project folder into ComfyUI's custom nodes directory, restarting ComfyUI, and starting the server inside LM Studio itself. It only works with vision-capable models when captioning images, and currently only processes the first image in a batch, though the author notes that batch support could be added if requested.
Custom ComfyUI nodes that let you use a locally running LM Studio model for image captioning or text generation, with no cloud calls or API keys.
Mainly Python. The stack also includes Python, ComfyUI, LM Studio.
Setup difficulty is rated moderate, with roughly 30min to a first successful run.
Mainly developer.
This repo across BitVibe Labs
double-check against the repo, no cap.