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How to Configure LTX-2 in ComfyUI: Complete 2026 Guide for AI Video Generation

#webdev #ai #ltx2

Introduction

LTX-2 represents a breakthrough in open-source AI video generation. Developed by Lightricks, this 19-billion parameter diffusion transformer model generates synchronized video and audio in a single pass, creating cohesive multimedia experiences that were previously only possible with proprietary systems. With native ComfyUI integration and NVIDIA-optimized checkpoints, LTX-2 brings professional-grade video generation to consumer hardware.

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This comprehensive guide walks you through configuring LTX-2 in ComfyUI, from initial installation to advanced workflow optimization. Whether you're new to AI video generation or an experienced ComfyUI user, you'll learn how to harness LTX-2's full potential for creating stunning synchronized audio-visual content.

What you'll learn:

  • Installing ComfyUI and LTX-2 custom nodes
  • Downloading and organizing required models
  • Configuring text-to-video and image-to-video workflows
  • Optimizing performance with NVFP4/FP8 quantization
  • Troubleshooting common issues

What is LTX-2?

LTX-2 is an open-source audio-video foundation model built on a Diffusion Transformer (DiT) architecture. Unlike traditional video generation models that create silent videos, LTX-2 generates motion, dialogue, sound effects, and music simultaneously, ensuring perfect synchronization between visual and audio elements.

Key Features

Synchronized Audio-Video Generation: LTX-2's unique architecture generates both modalities together, eliminating the need for separate audio synthesis and synchronization steps.

Multiple Model Variants: Choose the right checkpoint for your hardware and quality requirements:

Model Description Use Case
ltx-2-19b-dev Full model, bf16 format Training and fine-tuning
ltx-2-19b-dev-fp8 FP8 quantized Balanced quality and speed
ltx-2-19b-dev-fp4 NVFP4 quantized 3x faster, 60% less VRAM
ltx-2-19b-distilled 8-step distilled Fast generation, CFG=1
ltx-2-19b-distilled-lora-384 LoRA version Fine-tuning and customization

Advanced Control Options: Beyond basic text-to-video, LTX-2 supports:

  • Image-to-video generation with first-frame conditioning
  • Depth-based structural guidance
  • Pose-driven character animation
  • Canny edge control for precise motion

Upscaling Capabilities: Dedicated upscaler models enhance output quality:

  • Spatial upscaler (2x resolution)
  • Temporal upscaler (2x frame rate)

Technical Specifications

  • Architecture: Diffusion Transformer (DiT)
  • Parameters: 19 billion
  • License: ltx-2-community-license-agreement (open source)
  • Text Encoder: Gemma 3 12B IT (quantized to Q4_0)
  • Output: Synchronized video and audio

Limitations to Consider

While LTX-2 is powerful, be aware of these constraints:

  • Cannot provide factual information (it's a generative model, not a knowledge base)
  • May amplify societal biases present in training data
  • Prompt adherence varies; complex scenes may not match descriptions perfectly
  • Can generate inappropriate content; use content filtering for production
  • Audio quality degrades when generating speech-free content

System Requirements

LTX-2 is resource-intensive. Ensure your system meets these specifications before proceeding.

Minimum Hardware Requirements

GPU: NVIDIA GPU with 32GB VRAM

  • RTX 4090 (24GB) can run with optimizations
  • RTX 6000 Ada (48GB) recommended for full workflows

RAM: 32GB system memory minimum

  • 64GB recommended for complex workflows

Storage: 100GB+ free disk space

  • Models: ~50GB
  • Cache and temporary files: ~30GB
  • Working space for outputs: ~20GB

Operating System:

  • Windows 10/11 (64-bit)
  • Linux (Ubuntu 20.04+ or equivalent)
  • macOS (limited support, CPU-only)

Software Prerequisites

Python: Version 3.12 or higher

  • LTX-2 requires modern Python features
  • Virtual environment recommended

CUDA: Version 12.7 or higher

  • Required for GPU acceleration
  • Download from NVIDIA website

PyTorch: Version 2.7 or compatible

  • Will be installed with ComfyUI dependencies

Git: For cloning repositories

  • Windows: Git for Windows
  • Linux/Mac: Pre-installed or via package manager

Recommended Specifications for Optimal Performance

For the best experience, especially with real-time workflows:

  • GPU: NVIDIA RTX 4090 or A6000
  • RAM: 64GB DDR4/DDR5
  • Storage: NVMe SSD with 200GB+ free space
  • CPU: Modern multi-core processor (8+ cores)

Step 1: Install ComfyUI

ComfyUI provides the node-based interface for running LTX-2. If you already have ComfyUI installed, skip to Step 2.

Method A: Fresh Installation (Recommended for Beginners)

Windows Installation:

  1. Clone the ComfyUI repository: 
git clone https://github.com/comfyanonymous/ComfyUI.git
cd ComfyUI
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  1. Create a Python virtual environment: 
python -m venv venv
venv\Scripts\activate
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  1. Install dependencies: 
pip install -r requirements.txt
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  1. Install PyTorch with CUDA support: 
pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu121
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  1. Launch ComfyUI: 
python main.py
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Linux Installation:

  1. Clone and navigate: 
git clone https://github.com/comfyanonymous/ComfyUI.git
cd ComfyUI
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  1. Create virtual environment: 
python3 -m venv venv
source venv/bin/activate
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  1. Install dependencies: 
pip install -r requirements.txt
pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu121
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  1. Launch ComfyUI: 
python main.py
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Method B: Update Existing Installation

If you already have ComfyUI, update to the latest nightly version for LTX-2 compatibility:

cd ComfyUI
git pull origin master
pip install -r requirements.txt --upgrade
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Verify Installation

  1. Open your web browser and navigate to http://localhost:8188
  2. You should see the ComfyUI interface with a default workflow
  3. If the page loads successfully, ComfyUI is ready

Troubleshooting: If ComfyUI doesn't start:

  • Check Python version: python --version (should be 3.12+)
  • Verify CUDA installation: nvidia-smi
  • Check port availability: Try python main.py --port 8189

Step 2: Install LTX-2 Custom Nodes

LTX-2 requires custom nodes to integrate with ComfyUI. The easiest installation method uses ComfyUI Manager.

Method A: Install via ComfyUI Manager (Recommended)

  1. Open ComfyUI Manager:

    • Launch ComfyUI (python main.py)
    • Press Ctrl+M (Windows/Linux) or Cmd+M (Mac)
    • The Manager window will appear

  2. Search for LTXVideo:

    • Click "Install Custom Nodes"
    • Type "LTXVideo" in the search box
    • Find "ComfyUI-LTXVideo" by Lightricks

  3. Install the nodes:

    • Click the "Install" button
    • Wait for installation to complete (may take 2-5 minutes)
    • You'll see a success message when done

  4. Restart ComfyUI:

    • Close the ComfyUI terminal/window
    • Restart with python main.py
    • The LTXVideo nodes will appear in the node menu

Method B: Manual Installation

If ComfyUI Manager isn't available:

  1. Navigate to custom nodes directory: 
cd ComfyUI/custom_nodes
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  1. Clone the LTXVideo repository: 
git clone https://github.com/Lightricks/ComfyUI-LTXVideo.git
cd ComfyUI-LTXVideo
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  1. Install dependencies: 
pip install -r requirements.txt
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  1. Return to ComfyUI root and restart: 
cd ../..
python main.py
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Verify Node Installation

After restarting ComfyUI:

  1. Right-click in the workflow canvas
  2. Navigate to "Add Node" → "LTXVideo"
  3. You should see nodes like:
    • LTXVConditioning
    • LTX LTXV Add Guide
    • LTXVLoader
    • And others

If you see these nodes, installation was successful!

Low VRAM Configuration

If you have less than 32GB VRAM, use the low VRAM loader nodes:

  1. Locate low VRAM nodes: Look for nodes from low_vram_loaders.py
  2. Launch with VRAM reservation: 
python main.py --reserve-vram 5
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Replace 5 with the GB of VRAM to reserve for other processes.

Step 3: Download Required Models

LTX-2 requires several model files. On first use, ComfyUI will attempt to download them automatically, but manual download ensures you have the right versions.

Model Storage Locations

ComfyUI organizes models in specific directories:

ComfyUI/
├── models/
│   ├── checkpoints/              # Main LTX-2 models
│   ├── text_encoders/            # Gemma 3 12B encoder
│   │   └── gemma-3-12b-it-qat-q4_0-unquantized/
│   ├── latent_upscale_models/    # Upscaler models
│   └── loras/                    # LoRA control models (optional)
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Core Models to Download

1. Main Checkpoint (Choose One):

For most users, start with the FP8 quantized model for balanced performance:

  • ltx-2-19b-dev-fp8 (Recommended)
    • Download: Hugging Face
    • Size: ~19GB
    • Place in: ComfyUI/models/checkpoints/

Alternative options:

  • ltx-2-19b-distilled: Faster, 8-step generation
  • ltx-2-19b-dev-fp4: Lowest VRAM usage (NVIDIA GPUs only)

2. Text Encoder (Required):

  • Gemma 3 12B IT (Q4_0 quantized)
    • Download: Hugging Face
    • Size: ~7GB
    • Place in: ComfyUI/models/text_encoders/gemma-3-12b-it-qat-q4_0-unquantized/

3. Upscaler Models (Optional but Recommended):

  • Spatial Upscaler (2x)

    • Download: ltx-2-spatial-upscaler-x2-1.0
    • Place in: ComfyUI/models/latent_upscale_models/

  • Temporal Upscaler (2x)

    • Download: ltx-2-temporal-upscaler-x2-1.0
    • Place in: ComfyUI/models/latent_upscale_models/

Download Instructions

Using Git LFS (Recommended for Large Files):

# Install Git LFS if not already installed
git lfs install

# Clone the model repository
cd ComfyUI/models/checkpoints
git clone https://huggingface.co/Lightricks/LTX-2
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Using Hugging Face Hub:

pip install huggingface-hub

# Download specific model
huggingface-cli download Lightricks/LTX-2 ltx-2-19b-dev-fp8 --local-dir ComfyUI/models/checkpoints/
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Manual Download:

  1. Visit Hugging Face LTX-2 page
  2. Navigate to "Files and versions"
  3. Download required files
  4. Place in appropriate directories

Verify Model Installation

After downloading, verify your directory structure:

ComfyUI/models/checkpoints/ltx-2-19b-dev-fp8/
ComfyUI/models/text_encoders/gemma-3-12b-it-qat-q4_0-unquantized/
ComfyUI/models/latent_upscale_models/ltx-2-spatial-upscaler-x2-1.0/
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Important: Model files must match the expected naming conventions. If ComfyUI can't find models, check:

  • File names are exact (case-sensitive)
  • Files are in correct directories
  • No extra subdirectories were created during download

Step 4: Load Example Workflows

LTX-2 includes six pre-configured workflows that demonstrate different generation modes. These workflows are the fastest way to start creating content.

Accessing the Template Library

Method A: Via ComfyUI Interface:

  1. Open ComfyUI at http://localhost:8188
  2. Access Templates:
    • Click the "Load" button in the top menu
    • Navigate to "Template Library" → "Video"
    • Look for LTX-2 workflows

Method B: Download from GitHub:

cd ComfyUI
mkdir -p workflows/ltx2
cd workflows/ltx2

# Download example workflows
wget https://raw.githubusercontent.com/Comfy-Org/workflow_templates/main/video_ltx2_t2v.json
wget https://raw.githubusercontent.com/Comfy-Org/workflow_templates/main/video_ltx2_i2v.json
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Available Workflows

1. Text-to-Video (Full Model)

  • File: video_ltx2_t2v.json
  • Use Case: High-quality video generation from text prompts
  • Steps: 50 (adjustable)
  • Best For: Final production outputs

2. Text-to-Video (Distilled)

  • File: video_ltx2_t2v_distilled.json
  • Use Case: Fast preview generation
  • Steps: 8 (fixed)
  • Best For: Rapid iteration and testing

3. Image-to-Video (Full Model)

  • File: video_ltx2_i2v.json
  • Use Case: Animate still images with first-frame conditioning
  • Input: Single image + text prompt
  • Best For: Character animation, product demos

4. Image-to-Video (Distilled)

  • File: video_ltx2_i2v_distilled.json
  • Use Case: Quick image animation tests
  • Steps: 8
  • Best For: Previewing animation concepts

5. Video-to-Video Detailer

  • File: video_ltx2_v2v_detailer.json
  • Use Case: Enhance existing videos with additional detail
  • Input: Video file + enhancement prompt
  • Best For: Upscaling and refinement

6. IC-LoRA Multi-Control

  • File: video_ltx2_iclora_multicontrol.json
  • Use Case: Advanced control with multiple guidance types
  • Controls: Depth, Pose, Canny edges
  • Best For: Precise motion control

Loading a Workflow

  1. Download or locate the workflow JSON file
  2. In ComfyUI, click "Load" → "Load Workflow"
  3. Select the JSON file
  4. Wait for nodes to populate the canvas
  5. Check that all nodes are connected (no red error indicators)

If you see missing nodes errors:

  • Ensure LTXVideo custom nodes are installed
  • Restart ComfyUI
  • Check that models are in correct directories

Step 5: Configure Your First Generation

Let's create your first video using the Text-to-Video workflow. This section walks through each parameter and explains how to achieve the best results.

Understanding the T2V Workflow Structure

The Text-to-Video workflow consists of five main components:

  1. Text Encoding: Converts your prompt into embeddings
  2. Conditioning: Binds text with frame rate and other parameters
  3. Sampling: Generates the latent video representation
  4. Decoding: Converts latents to viewable video
  5. Audio-Video Muxing: Combines synchronized audio and video

Configuring the Prompt

Prompt Engineering for LTX-2:

LTX-2 responds best to descriptive, scene-focused prompts. Follow these guidelines:

Good Prompt Structure:

[Subject] [Action] [Setting] [Mood/Style] [Camera Movement] [Audio Description]
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Example Prompts:

Simple Scene:

A golden retriever puppy playing in a sunny garden, wagging its tail excitedly.
Gentle ambient sounds of birds chirping and leaves rustling. Slow camera pan.
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Complex Scene:

A 1950s diner waitress in a pink uniform serves coffee to customers at the counter.
Vintage aesthetic with warm lighting. Camera dollies from left to right.
Background chatter, clinking dishes, and upbeat jazz music.
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Prompt Tips:

  • Be specific about audio: LTX-2 generates better results when you describe desired sounds
  • Include camera movement: "Static shot", "Slow zoom", "Pan left to right"
  • Describe lighting: "Golden hour", "Neon lights", "Soft studio lighting"
  • Specify style: "Cinematic", "Documentary", "Vintage film"

Key Parameters Explained

1. Frame Rate (fps):

  • 24 fps: Cinematic look, standard for film
  • 30 fps: Smooth motion, good for general content
  • 60 fps: Very smooth, best for action scenes
  • Note: Higher fps requires more VRAM and processing time

2. Resolution:

  • Must be divisible by 32
  • Common options:
    • 512x512: Fast testing
    • 768x512: Widescreen preview
    • 1024x576: HD quality
    • 1280x720: Full HD (requires 48GB+ VRAM)

3. Number of Frames:

  • Must be divisible by (8 + 1) = 9
  • Examples: 9, 18, 27, 36, 45 frames
  • Longer videos require exponentially more VRAM

4. Sampling Steps:

  • Full model: 30-50 steps (higher = better quality)
  • Distilled model: 8 steps (fixed)
  • More steps = longer generation time

5. CFG Scale (Classifier-Free Guidance):

  • Range: 1.0 - 15.0
  • 1.0-3.0: Loose interpretation, creative
  • 5.0-7.0: Balanced (recommended)
  • 10.0+: Strict adherence, may reduce quality

Step-by-Step Generation Process

  1. Load the T2V workflow (as described in Step 4)

  2. Locate the CLIP Text Encode node:

    • This is where you enter your prompt
    • Type or paste your descriptive text

  3. Configure LTXVConditioning node:

    • Set frame rate (default: 24)
    • Adjust CFG scale (start with 7.0)

  4. Set resolution in the Sampler node:

    • Width: 768
    • Height: 512
    • Frames: 27 (for ~1 second at 24fps)

  5. Choose your checkpoint:

    • In the model loader node
    • Select ltx-2-19b-dev-fp8 for balanced performance

  6. Queue the generation:

    • Click "Queue Prompt" in the top right
    • Watch the progress bar
    • Generation time: 2-10 minutes depending on hardware

  7. Preview the result:

    • Video appears in the output node
    • Right-click to save or play

Audio Synchronization Settings

LTX-2 automatically generates synchronized audio. To control audio characteristics:

In your prompt, specify:

  • Type of sounds: "dialogue", "music", "ambient sounds"
  • Audio mood: "upbeat", "melancholic", "energetic"
  • Volume balance: "quiet background music", "prominent dialogue"

Note: Audio quality is best when generating content with clear sound sources (dialogue, music). Silent or ambient-only scenes may have lower audio fidelity.

Advanced Features

Once you're comfortable with basic text-to-video generation, explore these advanced capabilities to gain precise control over your outputs.

Control-to-Video: Depth, Pose, and Canny

LTX-2 supports three types of structural guidance for precise motion control:

1. Depth-Based Control

Use depth maps to guide spatial structure and camera movement:

  • Workflow: Load video_ltx2_depth_control.json
  • Preprocessor: "Image to Depth Map (Lotus)"
  • Use Cases:
    • Maintaining consistent 3D structure
    • Controlling camera perspective changes
    • Architectural walkthroughs

Setup:

  1. Load your reference image
  2. Apply Lotus depth preprocessor
  3. Connect depth map to LTX LTXV Add Guide node
  4. Set guidance strength (0.5-1.0)

2. Pose-Driven Animation

Control character movement with pose estimation:

  • Workflow: Load video_ltx2_pose_control.json
  • Preprocessor: DWPreprocessor (DWPose)
  • Use Cases:
    • Character animation
    • Dance sequences
    • Action choreography

Setup:

  1. Input reference video or image sequence
  2. Extract poses with DWPreprocessor
  3. Optional: Load Pose Control LoRA for enhanced accuracy
  4. Connect to guidance node

3. Canny Edge Control

Use edge detection for structural guidance:

  • Workflow: Load video_ltx2_canny_control.json
  • Preprocessor: Canny edge detector
  • Use Cases:
    • Preserving object boundaries
    • Architectural details
    • Line art animation

Setup:

  1. Apply Canny edge detection to reference
  2. Adjust threshold values (low: 100, high: 200)
  3. Connect edges to guidance node
  4. Balance with text prompt strength

Spatial and Temporal Upscaling

Enhance your generated videos with dedicated upscaler models:

Spatial Upscaler (2x Resolution):

  1. Add upscaler node after initial generation
  2. Load model: ltx-2-spatial-upscaler-x2-1.0
  3. Connect latent output to upscaler input
  4. Result: 768x512 → 1536x1024

Benefits:

  • Sharper details
  • Better texture quality
  • Minimal artifacts

Temporal Upscaler (2x Frame Rate):

  1. Add temporal upscaler node
  2. Load model: ltx-2-temporal-upscaler-x2-1.0
  3. Connect video output
  4. Result: 24fps → 48fps

Benefits:

  • Smoother motion
  • Reduced judder
  • Better slow-motion capability

Combining Both:
Chain spatial and temporal upscalers for maximum quality:

  • Input: 768x512 @ 24fps
  • After spatial: 1536x1024 @ 24fps
  • After temporal: 1536x1024 @ 48fps

Note: Upscaling significantly increases VRAM usage and processing time.

LoRA Training and Application

Fine-tune LTX-2 for specific styles or subjects:

Training Your Own LoRA:

  1. Prepare dataset: 10-50 video clips of your target style
  2. Use LTX-2 Trainer: Follow official training guide
  3. Training time: 1-2 hours on modern GPUs
  4. Output: LoRA weights file

Applying LoRA in ComfyUI:

  1. Place LoRA in ComfyUI/models/loras/
  2. Add LoRA Loader node to workflow
  3. Set strength: 0.5-1.0 (higher = stronger effect)
  4. Connect to model input

IC-LoRA (Image-Conditioned LoRA):

Special LoRA type that uses reference images:

  • Load video_ltx2_iclora_multicontrol.json
  • Provide reference image
  • Combine with other controls (depth, pose, canny)
  • Achieve consistent character appearance

Performance Optimization

Maximize generation speed and quality with these optimization techniques.

NVFP4/FP8 Quantization

NVIDIA's optimized checkpoints offer significant performance improvements:

FP8 Quantization (Recommended):

  • Model: ltx-2-19b-dev-fp8
  • VRAM Savings: ~30% compared to bf16
  • Speed: ~2x faster
  • Quality: Minimal degradation

NVFP4 Quantization (Maximum Speed):

  • Model: ltx-2-19b-dev-fp4
  • VRAM Savings: 60% compared to bf16
  • Speed: 3x faster
  • Quality: Slight quality reduction
  • Requirement: NVIDIA RTX 40-series or newer

Choosing the Right Quantization:

  • 32GB+ VRAM: Use FP8 for best balance
  • 24GB VRAM: Use NVFP4 for feasibility
  • 48GB+ VRAM: Consider bf16 for maximum quality

Multi-GPU Configuration

Distribute workload across multiple GPUs:

Sequence Parallelism:

  1. Edit ComfyUI launch script: 
python main.py --multi-gpu --gpu-ids 0,1
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  1. Configure in workflow:
  2. Add "Multi-GPU Sampler" node
  3. Specify GPU allocation
  4. Balance VRAM usage

Benefits:

  • 2x GPUs: ~1.7x speed improvement
  • 4x GPUs: ~3x speed improvement
  • Enables higher resolutions

Memory Management Techniques

Tiled Decoding:
Reduce VRAM usage during video decoding:

  1. Add "Tiled VAE Decode" node
  2. Set tile size: 512x512
  3. Overlap: 64 pixels
  4. Slower but uses 50% less VRAM

Model Offloading:
For systems with limited VRAM:

python main.py --lowvram
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Offloads models to RAM when not in use.

Batch Processing:
Generate multiple videos efficiently:

  • Queue multiple prompts
  • ComfyUI processes sequentially
  • Models stay loaded between generations
  • Faster than individual runs

Workflow Optimization Tips

  1. Use Distilled Models for Iteration:

    • Test prompts with 8-step distilled model
    • Switch to full model for final output
    • Saves 80% of iteration time

  2. Cache Text Encodings:

    • Reuse encoded prompts
    • Add "Save Text Encoding" node
    • Load cached encodings for variations

  3. Progressive Resolution:

    • Start at 512x512 for testing
    • Upscale to target resolution
    • Faster than direct high-res generation

Troubleshooting

Common issues and their solutions when working with LTX-2 in ComfyUI.

VRAM Out of Memory Errors

Symptoms: "CUDA out of memory" error during generation

Solutions:

  1. Reduce resolution: Try 512x512 or 768x512
  2. Decrease frame count: Use 18 or 27 frames instead of 36+
  3. Use NVFP4 model: Requires 60% less VRAM
  4. Enable low VRAM mode: 
python main.py --lowvram --reserve-vram 4
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  1. Use tiled decoding: Add Tiled VAE Decode node
  2. Close other applications: Free up GPU memory

Model Download Failures

Symptoms: "Model not found" or download timeout errors

Solutions:

  1. Manual download: Use Git LFS or Hugging Face CLI
  2. Check internet connection: Large files require stable connection
  3. Verify file paths: Ensure models are in correct directories
  4. Check disk space: Need 100GB+ free space
  5. Use mirror sites: Try alternative download sources

Missing Nodes Errors

Symptoms: Red nodes or "Node not found" messages

Solutions:

  1. Reinstall custom nodes: 
cd ComfyUI/custom_nodes/ComfyUI-LTXVideo
git pull
pip install -r requirements.txt --upgrade
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  1. Restart ComfyUI: Close and relaunch
  2. Check Python version: Must be 3.12+
  3. Verify dependencies: Run pip list to check installations

Audio-Video Synchronization Issues

Symptoms: Audio doesn't match video timing or is missing

Solutions:

  1. Check prompt: Explicitly describe audio in your prompt
  2. Verify muxing node: Ensure "Video Combine" node is connected
  3. Frame rate consistency: Use standard rates (24, 30, 60 fps)
  4. Regenerate: Audio generation can be inconsistent; try again
  5. Use full model: Distilled model may have lower audio quality

Slow Generation Times

Symptoms: Generation takes 10+ minutes for short clips

Solutions:

  1. Use distilled model: 8-step generation is 5-6x faster
  2. Enable NVFP4: 3x speed improvement on compatible GPUs
  3. Reduce resolution: Lower resolution = faster generation
  4. Check GPU utilization: Use nvidia-smi to verify GPU is active
  5. Update drivers: Ensure latest NVIDIA drivers installed

Poor Output Quality

Symptoms: Blurry, artifacts, or inconsistent results

Solutions:

  1. Increase sampling steps: Try 40-50 steps for full model
  2. Adjust CFG scale: Test range 5.0-9.0
  3. Improve prompt: Be more specific and descriptive
  4. Use higher resolution: 768x512 minimum for quality
  5. Try different checkpoint: FP8 vs NVFP4 vs distilled
  6. Add upscaler: Use spatial upscaler for sharper output

Conclusion

LTX-2 brings professional-grade synchronized audio-video generation to ComfyUI, making advanced AI video creation accessible on consumer hardware. By following this guide, you've learned how to:

  • Install and configure ComfyUI with LTX-2 custom nodes
  • Download and organize the required models
  • Create your first text-to-video and image-to-video generations
  • Apply advanced controls like depth, pose, and canny guidance
  • Optimize performance with quantization and multi-GPU setups
  • Troubleshoot common issues

Key Takeaways

Start Simple: Begin with the distilled model and low resolutions to learn the workflow quickly. Once comfortable, move to the full model for production-quality outputs.

Experiment with Prompts: LTX-2's audio-video synchronization shines when you describe both visual and audio elements in detail. Spend time crafting descriptive prompts.

Optimize for Your Hardware: Choose the right checkpoint (FP8, NVFP4, or distilled) based on your VRAM availability. Don't hesitate to use low VRAM modes if needed.

Leverage Advanced Features: Once you master basic generation, explore control-to-video workflows for precise motion control and upscalers for enhanced quality.

Learning Resources

Official Documentation:

Community Resources:

Online Demos:

What's Next?

Now that you have LTX-2 configured, consider these next steps:

  1. Create a Portfolio: Generate diverse videos to understand LTX-2's capabilities
  2. Train Custom LoRAs: Fine-tune for your specific style or subject matter
  3. Explore Control Methods: Master depth, pose, and canny guidance
  4. Join the Community: Share your work and learn from others
  5. Stay Updated: LTX-2 is actively developed; watch for new features

The future of AI video generation is here, and with LTX-2 in ComfyUI, you have the tools to create stunning synchronized audio-visual content. Happy generating!

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