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.

ltx-2.mp4 🚀 Quick Start

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 Required Models Download the following models from the LTX-2.3 HuggingFace repository:

LTX-2.3 Model Checkpoint (choose and download one of the following)

ltx-2.3-22b-dev.safetensors - Download ltx-2.3-22b-distilled-1.1.safetensors - Download Spatial Upscaler - Required for current two-stage pipeline implementations in this repository

ltx-2.3-spatial-upscaler-x2-1.1.safetensors - Download ltx-2.3-spatial-upscaler-x1.5-1.0.safetensors - Download Temporal Upscaler - Supported by the model and will be required for future pipeline implementations

ltx-2.3-temporal-upscaler-x2-1.0.safetensors - Download Distilled LoRA - Required for current two-stage pipeline implementations in this repository (except DistilledPipeline, ICLoraPipeline, and LipDubPipeline)

ltx-2.3-22b-distilled-lora-384-1.1.safetensors - Download Gemma Text Encoder (download all assets from the repository)

Gemma 3 LoRAs

LTX-2.3-22b-IC-LoRA-Union-Control - Download LTX-2.3-22b-IC-LoRA-Motion-Track-Control - Download LTX-2-19b-IC-LoRA-Detailer - Download LTX-2-19b-IC-LoRA-Pose-Control - Download LTX-2-19b-LoRA-Camera-Control-Dolly-In - Download LTX-2-19b-LoRA-Camera-Control-Dolly-Left - Download LTX-2-19b-LoRA-Camera-Control-Dolly-Out - Download LTX-2-19b-LoRA-Camera-Control-Dolly-Right - Download LTX-2-19b-LoRA-Camera-Control-Jib-Down - Download LTX-2-19b-LoRA-Camera-Control-Jib-Up - Download LTX-2-19b-LoRA-Camera-Control-Static - Download LTX-2.3-22b-IC-LoRA-HDR - HDR IC-LoRA and pre-computed text embeddings for HDRICLoraPipeline LTX-2.3-22b-IC-LoRA-LipDub - Download Available Pipelines TI2VidTwoStagesPipeline - Production-quality text/image-to-video with 2x upsampling (recommended) TI2VidTwoStagesHQPipeline - Same two-stage flow as above but uses the res_2s second-order sampler (fewer steps, better quality) TI2VidOneStagePipeline - Single-stage generation for quick prototyping DistilledPipeline - Fastest inference with 8 predefined sigmas ICLoraPipeline - Video-to-video and image-to-video transformations (uses distilled model.) KeyframeInterpolationPipeline - Interpolate between keyframe images A2VidPipelineTwoStage - Audio-to-video generation conditioned on an input audio file RetakePipeline - Regenerate a specific time region of an existing video HDRICLoraPipeline - Video-to-video with HDR output (linear float frames via LogC3 inverse decode, suitable for EXR export and tonemapping) LipDubPipeline - Lip dubbing, rephrasing, matching speaker identity (distilled model, single IC-LoRA, Two stages). ⚡ Optimization Tips Use DistilledPipeline - Fastest inference with only 8 predefined sigmas (8 steps stage 1, 4 steps stage 2) Enable FP8 quantization - Enables lower memory footprint: --quantization fp8-cast (CLI) or quantization=QuantizationPolicy.fp8_cast() (Python). Fp8-cast should be used with bf16 checkpoints, it shall downcast them on the fly. For Hopper GPUs with TensorRT-LLM, use --quantization fp8-scaled-mm for FP8 scaled matrix multiplication. Fp8-scaled-mm should be used with fp8 checkpoints. 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 TI2VidOneStagePipeline for faster generation when high resolution isn't required ✍️ Prompting for LTX-2 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

Automatic Prompt Enhancement LTX-2 pipelines support automatic prompt enhancement via an enhance_prompt parameter.

🔌 ComfyUI Integration To use our model with ComfyUI, please follow the instructions at https://github.com/Lightricks/ComfyUI-LTXVideo/.

📦 Packages 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.

📚 Documentation 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