ComfyUI-LTXVideo

ComfyUI-LTXVideo
★ 3,356

视频生成LoRA多条件控制加速省显存
为ComfyUI提供LTX-Video自定义节点，使用单一联合LoRA同时支持深度与边缘等多条件控制，在下采样潜空间处理以节省显存并加速推理，保持质量。
💡 用单个LoRA在ComfyUI里对视频依据深度或边缘进行高效控制和推理。
🍴 353 Forks💻 Python🔄 2026-03-06
📦
网盘下载
复制链接后前往夸克网盘下载
https://pan.quark.cn/s/a9fb3a59e10c
📦 requirements.txt
diffusers
einops
huggingface_hub>=0.25.2
ninja~=1.11.1.4
transformers[timm]>=4.50.0
Model
📄 README

ComfyUI-LTXVideo

[](https://github.com/Lightricks/LTX-2)

[](https://ltx.io/model)

[](https://huggingface.co/Lightricks/LTX-2.3)

[](https://github.com/Lightricks/LTX-2/tree/main/packages/ltx-trainer)

[](https://app.ltx.studio/ltx-2-playground/i2v)

[](https://videos.ltx.io/LTX-2/grants/LTX_2_Technical_Report_compressed.pdf)

[](https://discord.gg/ltxplatform)

A collection of powerful custom nodes that extend ComfyUI’s capabilities for the LTX-2 video generation model.

LTX-2 is built into ComfyUI core (see it here), making it readily accessible to all ComfyUI users. This repository hosts additional nodes and workflows to help you get the most out of LTX-2’s advanced features.

To learn more about LTX-2 See the main LTX-2 repository for model details and additional resources.

Prerequisites

Before you begin using an LTX-2 workflow in ComfyUI, make sure you have:

  • ComfyUI installed (Download here](https://www.comfy.org/download)
  • CUDA-compatible GPU with 32GB+ VRAM
  • 100GB+ free disk space for models and cache

    • Quick Start 🚀

    We recommend using the LTX-2 workflows available in Comfy Manager.

  • Open ComfyUI
  • Click the Manager button (or press Ctrl+M)
  • Select Install Custom Nodes
  • Search for “LTXVideo”
  • Click Install
  • Wait for installation to complete
  • Restart ComfyUI

    • The nodes will appear in your node menu under the “LTXVideo” category. Required models will be downloaded on first use.

    Example Workflows

    The ComfyUI-LTXVideo installation includes several example workflows.

    You can see them all at:

    ComfyUI/custom_nodes/ComfyUI-LTXVideo/example_workflows/

    LTX-2.3 Workflows:

  • Text/image to video full/distilled model; single stage
  • Text/image to video distilled model; two stages (with upsampling)
  • IC-LoRA distilled model depth + human pose + edges
  • IC-LoRA distilled model I2V motion tracking

    • Older Workflows (LTX-2.0):

  • Text to video full model
  • Text to video distilled model (Fast)
  • Image to video full model
  • Image to video distilled model (Fast)
  • Video to video detailer
  • IC-LoRA distilled model (depth + human pose + edges)
  • IC-LoRA distilled model with downscaled reference latents

    • Union IC-LoRA Model

    We introduce a new Union IC-LoRA model that combines depth and edge (canny) control conditions into a single unified LoRA.

    Key Features

  • Unified Control: A single LoRA that supports multiple control conditions (depth or edges).
  • Downsampled Latent Processing: The union LoRA operates on a downsampled latent size, which reduces memory usage and significantly speeds up inference while maintaining quality.

    • How It Works

    The union LoRA is trained to understand and respond to both control signals (depth maps and edge maps) within a single model. The model learns to:

  • Parse multiple conditions: Identify which control signals are present in the input
  • Process at reduced resolution: Work on downsampled latents to improve efficiency

    • Required Models

    Download the following models:

    LTX-2.3 Model Checkpoint – Choose and download one of the models to COMFYUI_ROOT_FOLDER/models/checkpoints folder.

  • ltx-2.3-22b-dev.safetensors
  • ltx-2.3-22b-distilled.safetensors

    • Spatial Upscaler – Required for current two-stage pipeline implementations in this repository. Download to COMFYUI_ROOT_FOLDER/models/latent_upscale_models folder.

  • ltx-2.3-spatial-upscaler-x2-1.0.safetensors
  • ltx-2.3-spatial-upscaler-x1.5-1.0.safetensors

    • Temporal Upscaler – Required for current two-stage pipeline implementations in this repository. Download to COMFYUI_ROOT_FOLDER/models/latent_upscale_models folder.

  • ltx-2.3-temporal-upscaler-x2-1.0.safetensors

    • Distilled LoRA – Required for current two-stage pipeline implementations in this repository (except DistilledPipeline and ICLoraPipeline). Download to COMFYUI_ROOT_FOLDER/models/loras folder.

  • ltx-2.3-22b-distilled-lora-384.safetensors

    • Gemma Text Encoder Download all files from the repository to COMFYUI_ROOT_FOLDER/models/text_encoders/gemma-3-12b-it-qat-q4_0-unquantized.

  • Gemma 3

    • LoRAs Choose and download to COMFYUI_ROOT_FOLDER/models/loras folder.

  • ltx-2.3-22b-ic-lora-union-control-ref0.5.safetensors
  • ltx-2.3-22b-ic-lora-motion-track-control-ref0.5.safetensors
  • ltx-2-19b-ic-lora-detailer.safetensors
  • ltx-2-19b-ic-lora-pose-control.safetensors
  • ltx-2-19b-lora-camera-control-dolly-in.safetensors
  • ltx-2-19b-lora-camera-control-dolly-left.safetensors
  • ltx-2-19b-lora-camera-control-dolly-out.safetensors
  • ltx-2-19b-lora-camera-control-dolly-right.safetensors
  • ltx-2-19b-lora-camera-control-jib-down.safetensors
  • ltx-2-19b-lora-camera-control-jib-up.safetensors
  • ltx-2-19b-lora-camera-control-static.safetensors

    • Advanced Techniques

    Low VRAM

  • For systems with low VRAM you can use the model loader nodes from low_vram_loaders.py. Those nodes ensure the correct order of execution and perform the model offloading such that generation fits in 32 GB VRAM.
  • Use –reserve-vram ComfyUI parameter: python -m main --reserve-vram 5 (or other number in GB).
  • For complete information about using LTX-2 models, workflows, and nodes in ComfyUI, please visit our Open Source documentation.