LMStudio Prompt Ovum

LMStudioPromptOvum is a full client for LM Studio's local server tucked inside a single ComfyUI node. Wire it up, and you can have a local LLM rewrite your prompt, describe an image, or generate true/false answers mid-workflow - all through the standard OpenAI-compatible endpoint, with no cloud account and no API key.

The appeal is obvious once you've wrestled with prompt chains by hand: instead of hardcoding prompt variations, a local model can generate them for you. And because LM Studio runs everything on your own machine, there's no cost spiral and no data leaving the box. The name is not a lie about needing a server - you do need LM Studio running with its server enabled - but it's a lie about effort: no key, no registration, just a local process.

What it needs before anything works

Three things, all from the node's own description and the README:

1. LM Studio running with the local server enabled (Settings → Developer → Server), listening on localhost:1234 by default.
2. Models downloaded inside LM Studio. The selected_model combo is auto-populated from whatever LM Studio reports, and that list is cached between sessions.
3. For auto-unload specifically: install the tiny model liquid/lfm2-1.2b and set LM Studio's automatic model unloading to its shortest period (60 seconds). This is a documented quirk, not optional: LM Studio doesn't support unloading a model on request, so the node does a "hack" - it swaps to the tiny model to free VRAM. If you don't need auto-unload, set unload_timeout_seconds to 0 and skip this.

The inputs that matter

• input_prompt - the main prompt (multiline). Supports wildcard syntax {option1|option2} for random selection.
• mode - how to process the prompt: prompt (direct), descriptor (adds random descriptive words), pixelwave / style / character (predefined conversation templates), custom (uses custom_history), plus none and true-or-false.
• server_address / server_port - defaults localhost / 1234.
• selected_model - which downloaded model to use; enable dynamic loading and it'll be loaded on demand.
• seed - 0 for random, or a fixed number for reproducible output and reproducible wildcard picks.
• unload_timeout_seconds - auto-unload after this many seconds of inactivity (0 disables). Requires the liquid/lfm2-1.2b model as above.
• image (optional) - attach an IMAGE and it's encoded and sent along, so vision-capable models can describe or evaluate it.

Two outputs: text (STRING, the model's reply) and context (LLM_CONTEXT, a chat-history bundle you feed to LMStudioPromptChainOvum to keep a conversation going).

Where you'll use it

The canonical setup: generate a prompt, feed text into your CLIP text encoder, and let the local model riff on themes or styles per run. The descriptor and pixelwave modes are clearly tuned for that. Add an image and you've got automatic image captioning/description in the loop. The true-or-false mode is a cheap way to build a conditional branch on a yes/no question - wire the reply through a compare and route the graph.

Troubleshooting

• "Connection refused" / no response - LM Studio server isn't running, or the port/address don't match. Confirm the server is actually enabled in LM Studio.
• selected_model shows "No models available" - nothing is loaded in LM Studio or the server isn't reporting; load a model first, then refresh.
• Auto-unload silently does nothing - check that liquid/lfm2-1.2b is installed and LM Studio's auto-unload is set short. The README is explicit that both are required.
• Community reports (r/StableDiffusion, r/comfyui) confirm the general pattern works well with Qwen3-VL-class models for vision tasks - the flakiest part is always the server, not the node.

Install

The node ships in comfy-ovum:

cd ComfyUI/custom_nodes
git clone https://github.com/sfinktah/comfy-ovum

or ComfyUI Manager → comfy-ovum, restart. Then install LM Studio separately, download models, and enable its server. The pack's Python deps include requests, which is what does the talking to the server - Manager handles it.