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 repo
git clone https://github.com/Lightricks/LTX-2.git
cd LTX-2
Download the relevant models or use the Hugging Face CLI
hf auth login
hf download Lightricks/LTX-2.3 \
ltx-2.3-22b-distilled-1.1.safetensors ltx-2.3-spatial-upscaler-x2-1.1.safetensors --local-dir models/ltx-2.3
hf download google/gemma-3-12b-it-qat-q4_0-unquantized --local-dir models/gemma-3-12b
If you get a 401/403, accept the model terms on Hugging Face and log in with a Read token (fine-grained tokens need the "read gated repos" scope enabled).
Generate
uv run python -m ltx_pipelines.distilled \
--distilled-checkpoint-path models/ltx-2.3/ltx-2.3-22b-distilled-1.1.safetensors \
--spatial-upsampler-path models/ltx-2.3/ltx-2.3-spatial-upscaler-x2-1.1.safetensors \
--gemma-root models/gemma-3-12b \
--seed 42 \
--output-path output.mp4 \
--prompt "A medium close-up shot features a Caucasian man with a beard, wearing a green and white baseball cap without any letters on the front, and a light blue shirt over a white t-shirt. He is positioned in the center of the frame, looking intently directly at the camera, his eyes focused on camera. His facial expression is one of deep concentration, with his brow slightly raised. As he looks straight at the camera, a quick sniff sound is heard, and then he speaks with a deep male voice and a satisfied tone, saying, 'I think it's so good.' The camera remains static throughout, maintaining a shallow depth of field, which keeps the man in sharp focus while the background is softly blurred, showing a beige wall behind him. After a brief pause, another short, audible sniff is heard. The man then continues to speak, his voice maintaining the same quality, as he states, 'So good. So good.' He elaborates further, emphasizing his point with a final statement, 'This got to be, it's got to be the best tool I've ever seen.'"
In cases of GPU memory constraints, consider --quantization fp8-cast --offload {cpu, disk}. See additional flags.
This uses the distilled model and pipeline for fast results. For better quality or other capabilities, see Models and Pipelines.
Full Model List
For pipelines beyond the quickstart, download the relevant 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 - Downloadltx-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 - Downloadltx-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 DubItPipeline)
ltx-2.3-22b-distilled-lora-384-1.1.safetensors - Download
Gemma Text Encoder (download all assets from the repository)
LoRAs
LTX-2.3-22b-IC-LoRA-Union-Control - DownloadLTX-2.3-22b-IC-LoRA-Motion-Track-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 - DownloadLTX-2.3-22b-IC-LoRA-HDR - HDR IC-LoRA and pre-computed text embeddings for HDRICLoraPipelineLTX-2.3-22b-IC-LoRA-DubIt - 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)
- DubItPipeline - Dub-It: rephrasing while matching speaker identity and lip movements (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. On Hopper+ GPUs with native FP8 support, 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 Hopper GPUs, install the FlashAttention 3 wheel. On other CUDA GPUs, PyTorch SDPA is used automatically. An installed backend is selected automatically at runtime; forcing a specific one is a Python-API option (AttentionFunction.FLASH_ATTENTION_3/FLASH_ATTENTION_4), not a CLI flag.
- 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.io/blog/prompting-guide-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
