What is ComfyUI-LTXVideo?
ComfyUI-LTXVideo is Lightricks' collection of custom nodes that extends ComfyUI for the LTX-2 video generation model. It sits in the Video Generation category of the Multimodal Generation tools and is open-source, written in Python. To get started: check the prerequisites.
Overview
ComfyUI-LTXVideo is Lightricks' collection of custom nodes that extends ComfyUI for the LTX-2 video generation model. LTX-2 itself is already built into ComfyUI core, so every ComfyUI user can reach the basic pipeline without this repository; what the node pack adds is the advanced surface — the control adapters, the staged workflows and the example graphs that get more out of the model than the built-in nodes expose.
The headline additions are IC-LoRA control workflows: depth, human pose and edge conditioning, motion tracking on image-to-video, HDR, a Dub-It pass and pixel spatial upscaling, plus a text-to-audio distilled workflow. A Union IC-LoRA combines depth and edge (canny) conditioning into a single adapter, and it operates on a downsampled latent, which cuts memory use and speeds up inference while holding quality. Single-stage and two-stage (upsampling) variants of the text/image-to-video graphs are both shipped.
Everything arrives as example workflow JSON files installed alongside the nodes, so the practical entry point is loading a graph rather than wiring one. The pack is installable from ComfyUI Manager and required model files are fetched on first use. It is hardware-hungry: the project lists a CUDA-compatible GPU with 32GB or more of VRAM and 100GB or more of free disk space for models and cache as prerequisites.
What it does
- IC-LoRA control workflows for depth, human pose and edge conditioning on LTX-2 generations
- A Union IC-LoRA that merges depth and canny control into one adapter and runs on a downsampled latent for lower memory use and faster inference
- Motion-tracking image-to-video, HDR, Dub-It and pixel spatial upscaling workflows
- Single-stage and two-stage (with upsampling) text/image-to-video graphs for both the full and distilled models
- A text-to-audio distilled workflow alongside the video graphs
- Example workflow JSON installed with the nodes, and models downloaded automatically on first use
Getting started
Install from ComfyUI Manager rather than cloning by hand, then load one of the bundled example workflows. Check the hardware requirements first — this is a large-model pipeline, not a laptop node pack.
Check the prerequisites
You need ComfyUI installed, a CUDA-compatible GPU with 32GB+ VRAM, and 100GB+ of free disk space for models and cache.
Install the nodes from ComfyUI Manager
Open ComfyUI, press Ctrl+M for the Manager, choose Install Custom Nodes, search for “LTXVideo”, install, and restart ComfyUI. The nodes then appear in the node menu under the LTXVideo category.
Manager (Ctrl+M) → Install Custom Nodes → search "LTXVideo" → Install → restartLoad an example workflow
The installation ships several ready-made graphs; the recommended starting point is one of the LTX-2 workflows available through Comfy Manager. Required models download on first use.
ComfyUI/custom_nodes/ComfyUI-LTXVideo/example_workflows/Move up to the control workflows
Once text-to-video works, switch to the IC-LoRA graphs for depth, pose and edge control, motion tracking, HDR or spatial upscaling — and use the two-stage distilled workflow when you want the upsampling pass.
Commands and code are distilled from the project's own documentation — always check the official repo for the latest.
When to use it
- Drive an LTX-2 generation with depth, pose or edge control instead of prompt text alone
- Add an upsampling second stage to a distilled LTX-2 run when the single-stage output is not sharp enough
- Use one Union IC-LoRA for depth and canny control to keep VRAM use and inference time down
- Start from a maintained example graph rather than reverse-engineering the model's node wiring yourself
How ComfyUI-LTXVideo compares
ComfyUI-LTXVideo alongside other open-source video generation tools AI/TLDR tracks, ranked by GitHub stars.
| Tool | Stars | What it does |
|---|---|---|
| MoneyPrinterTurbo | ★ 139k | A self-hosted WebUI, API and CLI that turns one topic or keyword into a finished short video — script, stock or generated footage, voiceover, subtitles and music. |
| HyperFrames | ★ 78.9k | HeyGen's HTML-to-video renderer: author a composition in HTML, CSS and JS, preview it in a browser, then render the same input to a deterministic MP4 locally or on AWS Lambda. |
| OpenMontage | ★ 76.5k | An agentic video production system that gives a coding assistant 12 pipelines and 100+ tools to research, script, generate and render a video end to end. |
| Open Generative AI | ★ 32.3k | MIT-licensed desktop and web studio for AI image, video and lip-sync generation that runs hosted models through the Muapi.ai API, with optional local image models in the desktop app. |
| Hypit | ★ 28k | A markup language and compiler that lets a coding agent rebuild a reference video as an editable workflow — footage, captions, B-roll and effects anchored to words rather than timestamps — and render variants in one command. |
| Toonflow | ★ 20.5k | Infinite-canvas workbench for AI short-drama production, taking a novel through chapter events, script, storyboards and video nodes. |
| waoowaoo | ★ 14.8k | Self-hosted creative workspace for AI image and video: a canvas holding references and generated assets, a side Assistant that develops the brief, and model-aware controls over your own OpenRouter key. |
| ComfyUI-LTXVideo | ★ 4.2k | Lightricks' custom ComfyUI nodes and workflows for the LTX-2 video model |