LTX-2 is the first DiT-based audio-video foundation model that contains all core capabilities of modern video generation in one model: synchronized audio and video, high fidelity, multiple performance modes, production-ready outputs, API access, and open access.
# Clone the repository
git clone https://github.com/Lightricks/LTX-2.git
cd LTX-2
# Set up the environment
uv sync --frozen
source .venv/bin/activate
Download the following models from the LTX-2 HuggingFace repository:
LTX-2 Model Checkpoint (choose and download one of the following)
Spatial Upscaler - Required for current two-stage pipeline implementations in this repository
Temporal Upscaler - Supported by the model and will be required for future pipeline implementations
Distilled LoRA - Required for current two-stage pipeline implementations in this repository (except DistilledPipeline and ICLoraPipeline)
Gemma Text Encoder (download all assets from the repository)
LoRAs
LTX-2-19b-IC-LoRA-Canny-Control- DownloadLTX-2-19b-IC-LoRA-Depth-Control- DownloadLTX-2-19b-IC-LoRA-Detailer- DownloadLTX-2-19b-IC-LoRA-Pose-Control- DownloadLTX-2-19b-LoRA-Camera-Control-Dolly-In- DownloadLTX-2-19b-LoRA-Camera-Control-Dolly-Left- DownloadLTX-2-19b-LoRA-Camera-Control-Dolly-Out- DownloadLTX-2-19b-LoRA-Camera-Control-Dolly-Right- DownloadLTX-2-19b-LoRA-Camera-Control-Jib-Down- DownloadLTX-2-19b-LoRA-Camera-Control-Jib-Up- DownloadLTX-2-19b-LoRA-Camera-Control-Static- Download
- TI2VidTwoStagesPipeline - Production-quality text/image-to-video with 2x upsampling (recommended)
- TI2VidOneStagePipeline - Single-stage generation for quick prototyping
- DistilledPipeline - Fastest inference with 8 predefined sigmas
- ICLoraPipeline - Video-to-video and image-to-video transformations
- KeyframeInterpolationPipeline - Interpolate between keyframe images
- Use DistilledPipeline - Fastest inference with only 8 predefined sigmas (8 steps stage 1, 4 steps stage 2)
- Enable FP8 transformer - Enables lower memory footprint:
--enable-fp8(CLI) orfp8transformer=True(Python) - Install attention optimizations - Use xFormers (
uv sync --extra xformers) or Flash Attention 3 for Hopper GPUs - Use gradient estimation - Reduce inference steps from 40 to 20-30 while maintaining quality (see pipeline documentation)
- Skip memory cleanup - If you have sufficient VRAM, disable automatic memory cleanup between stages for faster processing
- Choose single-stage pipeline - Use
TI2VidOneStagePipelinefor faster generation when high resolution isn't required
When writing prompts, focus on detailed, chronological descriptions of actions and scenes. Include specific movements, appearances, camera angles, and environmental details - all in a single flowing paragraph. Start directly with the action, and keep descriptions literal and precise. Think like a cinematographer describing a shot list. Keep within 200 words. For best results, build your prompts using this structure:
- Start with main action in a single sentence
- Add specific details about movements and gestures
- Describe character/object appearances precisely
- Include background and environment details
- Specify camera angles and movements
- Describe lighting and colors
- Note any changes or sudden events
For additional guidance on writing a prompt please refer to https://ltx.video/blog/how-to-prompt-for-ltx-2
LTX-2 pipelines support automatic prompt enhancement via an enhance_prompt parameter.
To use our model with ComfyUI, please follow the instructions at https://github.com/Lightricks/ComfyUI-LTXVideo/.
This repository is organized as a monorepo with three main packages:
- ltx-core - Core model implementation, inference stack, and utilities
- ltx-pipelines - High-level pipeline implementations for text-to-video, image-to-video, and other generation modes
- ltx-trainer - Training and fine-tuning tools for LoRA, full fine-tuning, and IC-LoRA
Each package has its own README and documentation. See the Documentation section below.
Each package includes comprehensive documentation:
- LTX-Core README - Core model implementation, inference stack, and utilities
- LTX-Pipelines README - High-level pipeline implementations and usage guides
- LTX-Trainer README - Training and fine-tuning documentation with detailed guides
