LTX-2

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.

🚀 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 - On datacenter Blackwell GPUs (B200), install FlashAttention 4 manually: uv pip install 'flash-attn-4==4.0.0b9' (this specific revision is the one we have verified against torch 2.9.1+cu128; newer betas have known issues on consumer Blackwell). On other CUDA GPUs (including Hopper), use xFormers (uv sync --extra xformers).
• 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