EA_LMStudio

The most fully-featured LM Studio integration for ComfyUI — auto model discovery, multi-image vision, reasoning extraction, structured JSON output, full sampler control, cancellable streaming, and built-in VRAM management.

Text-only: a prompt enhancer. The response renders inside the node, and troubleshooting reports exactly which parameters were applied plus full inference stats.

Vision: captioning an image with a VLM into schema-constrained JSON that downstream nodes can parse directly.

This node is ready to be integrated into your workflows in dozens of ways!

Features

• Auto Model Discovery - Models populate automatically from LM Studio at startup
• Vision Support - Up to 4 image inputs with smart auto-resize to prevent OOM
• Reasoning Extraction - Uses LM Studio's own reasoning split when the model has Reasoning Parsing configured, and falls back to tag detection (DeepSeek R1, Qwen3, QwQ, GLM, GPT-OSS "harmony" channels, and similar) when it does not
• Structured JSON Output - Constrain decoding to a JSON Schema so downstream nodes can parse the response
• Cancellable & Streamed - ComfyUI's cancel button really stops a runaway generation, and the queue progress bar tracks tokens instead of the node looking frozen
• In-Node Response Preview - The generated text appears inside the node; no extra preview node required
• Advanced Controls - Temperature, top-k/p, min-p, repetition penalty, stop strings, context-overflow policy, speculative decoding
• Honest Diagnostics - Every parameter is checked against the config LM Studio reports back, so a setting that silently does nothing is reported instead of hidden
• Detailed Stats - Tokens/sec, time to first token, stop reason, token counts, and speculative-decoding acceptance rates
• VRAM Management - Auto-unload after generation (enabled by default)

Installation

Via ComfyUI Manager (recommended): Search for "EA_LMStudio"

Manual:

cd ComfyUI/custom_nodes
git clone https://github.com/EnragedAntelope/EA_LMStudio.git
pip install -r EA_LMStudio/requirements.txt

Requirements

• LM Studio with the local server enabled. Validated against LM Studio 0.4.19.
• The lmstudio Python package (installed via requirements.txt). Requires Python 3.10+.

Keep LM Studio and the lmstudio package updated. The SDK discards prediction parameters it does not recognise instead of raising, so an out-of-date package can turn a setting into a silent no-op. The node now compares what it sent against the config LM Studio echoes back and reports any parameter that was dropped, in the troubleshooting output.

Quick Start

1. Start LM Studio with server enabled (default: http://127.0.0.1:1234)
2. Start ComfyUI
3. Find the node: EA -> LMStudio

Ready-made workflows live in example_workflows/ — drag one onto the ComfyUI canvas:

Workflow	What it shows
01-prompt-enhancer.json	Turning a short idea into a rich t2i prompt, with reasoning and stats previews
02-vision-caption-json.json	Captioning an image with a VLM into schema-constrained JSON

Tips

• Models not showing? LM Studio must be running before ComfyUI starts. Toggle the refresh_models checkbox to instantly re-fetch and update the dropdowns.
• Context errors? Increase context length in LM Studio settings (not max_tokens). Set context_overflow to Stop at limit (error) if a silently shortened prompt would be worse than no answer.
• VLM issues? Try a smaller image resize option or a single image if multi-image fails.
• Force thinking mode: Add /think to the prompt (Qwen3 family) or "Think step by step" for others. LM Studio's API has no thinking on/off flag — see below.
• Thinking leaking into the response? LM Studio splits reasoning from the answer only when the model's Reasoning Parsing delimiters are configured. Set them in LM Studio (per model) and the node uses that split directly. Otherwise it falls back to tag detection, and reports it when a tagless Thinking Process: preamble appears to have leaked through.
• Reducing repetition: Raise repeat_penalty (1.1-1.3) and/or use min_p (0.05-0.1) as a modern alternative to top_p. LM Studio has no presence or frequency penalty.
• Guaranteed parseable output: Set output_format to JSON (schema below) and supply a schema. JSON (no schema) only asks for JSON — it does not constrain decoding, and many models answer with a ```json fenced block (the node unwraps that automatically when the contents are valid JSON).
• Speculative decoding not helping? The troubleshooting output reports the draft-token acceptance rate. Below ~30% the draft model is usually costing more than it saves.

Custom Server

Edit lms_config/user_config.json:

{
    "server_host": "192.168.1.100",
    "server_port": 1234
}

Model Exclusion Patterns

Exclude models from the dropdown by adding patterns to lms_config/user_config.json:

{
    "excluded_model_patterns": ["embedding", "Qwen3-Coder", "codellama"]
}

• The default excludes all models containing "embedding".
• Patterns are matched as case-insensitive substrings against the full model identifier.
• Use specific enough patterns to avoid accidentally excluding desired models (e.g., use Qwen3-Coder instead of just coder).
• To find exact model names, check LM Studio's API: http://127.0.0.1:1234/v1/models (look at the id field).
• The user_config.json file is gitignored so your settings survive updates.
• Restart ComfyUI or toggle the refresh_models checkbox after changing patterns.

A model whose identifier contains characters the node will not accept (LM Studio occasionally serves ids like some-model@?) is hidden from the dropdown and named in the troubleshooting output, so it is never a silent disappearance.

Outputs

Output	Description
response	Generated text (reasoning removed if extracted)
reasoning	Extracted thinking content
troubleshooting	Status messages, debug hints, and inference stats (tokens/sec, input/output/total tokens, time to first token, stop reason, speculative-decoding acceptance, total time)

The response also renders inside the node itself, so a preview node is optional.

Upgrading from 1.x

Version 2.0.0 removed the presence_penalty and enable_thinking widgets. Neither ever did anything: the lmstudio SDK silently discards prediction-config keys it does not recognise, and LM Studio has no presence penalty and no thinking on/off flag. They were dropped before the request ever left your machine.

You do not need to rebuild anything. When a workflow saved by 1.x is loaded, the node detects the old layout and realigns every remaining setting to the right widget, showing a toast when it does. Without that, removing two mid-list widgets would have shifted every later value by one or two slots.

If you relied on enable_thinking, the equivalents that genuinely work are configuring Reasoning Parsing for the model in LM Studio, editing the model's Jinja template ({%- set enable_thinking = false %}), or a /think / /no_think marker in the prompt for Qwen3-family models.

License

MIT License

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Originally based on YANC_LMStudio by A Latent Place