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We're excited to announceLTX-2 - the next generation of LTX with synchronized audio+video generation!

LTX-2 is the first DiT-based audio-video foundation model that contains all core capabilities of modern video generation in one model.LTX-2 is now the primary home for LTX development and includes significant improvements:

• 🎵Synchronized Audio+Video Generation - Generate videos with perfectly synchronized audio
• 🎬Latest Model - LTX-2 with improved quality and capabilities
• 🔌ComfyUI Integration - Built into ComfyUI core for seamless workflows
• 🎯Advanced Features:
  • Multiple keyframe support
  • IC-LoRA control models for precise generation
  • Standard LoRA support for style customization
  • Latent upsampler for multiscale pipelines
• 🛠️Training Tools - LoRA training capabilities
• 📚Comprehensive Documentation - Full documentation athttps://docs.ltx.video
• 🔄Active Development - Ongoing improvements and community support

👉 Check out LTX-2 here

📖 View Documentation

• Introduction
• What's New
• Models
• Quick Start Guide
  • Online demo
  • Run locally
    • Installation
    • Inference
  • ComfyUI Integration
  • Diffusers Integration
• Model User Guide
• Community Contribution
• Training
• Control Models
• Join Us!
• Acknowledgement

LTX-Video is the first DiT-based video generation 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. It can generate up to 50 FPS videos at native 4K resolution with synchronized audio in one pass.The model is trained on a large-scale dataset of diverse videos and can generate high-resolution videos with realistic and diverse content.

The model supports image-to-video, multi-keyframe conditioning, keyframe-based animation, video extension (both forward and backward), video-to-video transformations, and any combination of these features.

Today we announced our newest foundation model, LTX-2. LTX-2 represents a major leap forward from our previous model, LTXV 0.9.8. Here’s what’s new:

• Audio + Video, Together: Visuals and sound are generated in one coherent process, with motion, dialogue, ambience, and music flowing simultaneously.
• 4K Fidelity: Professional-grade precision with native 4K and up to 50 fps, sharp textures, clean motion, and synchronized audio.
• Longer Generations: LTX-2 supports longer, continuous clips with synchronized audio up to 10 seconds.
• Low Cost & Efficiency: Up to 50% lower compute cost than competing models, powered by a multi-GPU inference stack.
• Creative Control: Multi-keyframe conditioning, 3D camera logic, and LoRA fine-tuning deliver frame-level precision and style consistency.

For more details, please see ourblog post. LTX-2 model weights, code, and benchmarks will be released to the community later in 2025.

• Long shot generation in LTXV-13B!
  • LTX-Video now supports up to 60 seconds of video.
  • Compatible also with the official IC-LoRAs.
  • Try now inComfyUI.
• Release a new distilled models:
  • 13B distilled modelltxv-13b-0.9.8-distilled
  • 2B distilled modelltxv-2b-0.9.8-distilled
  • Both models are distilled from the same base modelltxv-13b-0.9.8-dev and are compatible for use together in the same multiscale pipeline.
  • Improved prompt understanding and detail generation
  • Includes corresponding FP8 weights and workflows.
• Release a new detailer modelLTX-Video-ICLoRA-detailer-13B-0.9.8
  • Available inComfyUI.

• Released three new control models for LTX-Video on HuggingFace:
  • Depth Control:LTX-Video-ICLoRA-depth-13b-0.9.7
  • Pose Control:LTX-Video-ICLoRA-pose-13b-0.9.7
  • Canny Control:LTX-Video-ICLoRA-canny-13b-0.9.7

• Release a new 13B distilled modelltxv-13b-0.9.7-distilled
  • Amazing for iterative work - generates HD videos in 10 seconds, with low-res preview after just 3 seconds (on H100)!
  • Does not require classifier-free guidance and spatio-temporal guidance.
  • Supports sampling with 8 (recommended), or less diffusion steps.
  • Also released a LoRA version of the distilled model,ltxv-13b-0.9.7-distilled-lora128
    • Requires only 1GB of VRAM
    • Can be used with the full 13B model for fast inference
• Release a new quantized distilled modelltxv-13b-0.9.7-distilled-fp8 forreal-time generation (on H100) with even less VRAM

• Release a new 13B modelltxv-13b-0.9.7-dev
• Release a new quantized modelltxv-13b-0.9.7-dev-fp8 for faster inference with less VRam
• Release a new upscalers
  • ltxv-temporal-upscaler-0.9.7
  • ltxv-spatial-upscaler-0.9.7
• Breakthrough prompt adherence and physical understanding.
• New Pipeline for multi-scale video rendering for fast and high quality results

• Release a new checkpointltxv-2b-0.9.6-dev-04-25 with improved quality
• Release a new distilled modelltxv-2b-0.9.6-distilled-04-25
  • 15x faster inference than non-distilled model.
  • Does not require classifier-free guidance and spatio-temporal guidance.
  • Supports sampling with 8 (recommended), or less diffusion steps.
• Improved prompt adherence, motion quality and fine details.
• New default resolution and FPS: 1216 × 704 pixels at 30 FPS
  • Still real time on H100 with the distilled model.
  • Other resolutions and FPS are still supported.
• Support stochastic inference (can improve visual quality when using the distilled model)

• New license for commercial use (OpenRail-M)
• Release a new checkpoint v0.9.5 with improved quality
• Support keyframes and video extension
• Support higher resolutions
• Improved prompt understanding
• Improved VAE
• New online web app inLTX-Studio
• Automatic prompt enhancement

• Improve STG (Spatiotemporal Guidance) for LTX-Video
• Support MPS on macOS with PyTorch 2.3.0
• Add support for 8-bit model, LTX-VideoQ8
• Add TeaCache for LTX-Video
• AddComfyUI-LTXTricks
• Add Diffusion-Pipe

• Release theresearch paper

• Release a new checkpoint v0.9.1 with improved quality
• Support for STG / PAG
• Support loading checkpoints of LTX-Video in Diffusers format (conversion is done on-the-fly)
• Support offloading unused parts to CPU
• Support the new timestep-conditioned VAE decoder
• Reference contributions from the community in the readme file
• Relax transformers dependency

• Initial release of LTX-Video
• Support text-to-video and image-to-video generation

The model is accessible right away via the following links:

• LTX-Studio image-to-video (13B-mix)
• LTX-Studio image-to-video (13B distilled)
• Fal.ai image-to-video (13B full)
• Fal.ai image-to-video (13B distilled)
• Replicate image-to-video

The codebase was tested with Python 3.10.5, CUDA version 12.2, and supports PyTorch >= 2.1.2.On macOS, MPS was tested with PyTorch 2.3.0, and should support PyTorch == 2.3 or >= 2.6.

git clone https://github.com/Lightricks/LTX-Video.gitcd LTX-Video# create envpython -m venv envsource env/bin/activatepython -m pip install -e .\[inference\]

FP8 kernels developed for LTX-Video provide performance boost on supported graphics cards (Ada architecture and later). To install FP8 kernels, follow the instructions in that repository.

📝Note: For best results, we recommend using ourComfyUI workflow. We're working on updating the inference.py script to match the high quality and output fidelity of ComfyUI.

To use our model, please follow the inference code ininference.py:

python inference.py --prompt"PROMPT" --conditioning_media_paths IMAGE_PATH --conditioning_start_frames 0 --height HEIGHT --width WIDTH --num_frames NUM_FRAMES --seed SEED --pipeline_config configs/ltxv-13b-0.9.8-distilled.yaml

📝Note: Input video segments must contain a multiple of 8 frames plus 1 (e.g., 9, 17, 25, etc.), and the target frame number should be a multiple of 8.

python inference.py --prompt"PROMPT" --conditioning_media_paths VIDEO_PATH --conditioning_start_frames START_FRAME --height HEIGHT --width WIDTH --num_frames NUM_FRAMES --seed SEED --pipeline_config configs/ltxv-13b-0.9.8-distilled.yaml

You can now generate a video conditioned on a set of images and/or short video segments.Simply provide a list of paths to the images or video segments you want to condition on, along with their target frame numbers in the generated video. You can also specify the conditioning strength for each item (default: 1.0).

python inference.py --prompt"PROMPT" --conditioning_media_paths IMAGE_OR_VIDEO_PATH_1 IMAGE_OR_VIDEO_PATH_2 --conditioning_start_frames TARGET_FRAME_1 TARGET_FRAME_2 --height HEIGHT --width WIDTH --num_frames NUM_FRAMES --seed SEED --pipeline_config configs/ltxv-13b-0.9.8-distilled.yaml

fromltx_video.inferenceimportinfer,InferenceConfiginfer(InferenceConfig(pipeline_config="configs/ltxv-13b-0.9.8-distilled.yaml",prompt=PROMPT,height=HEIGHT,width=WIDTH,num_frames=NUM_FRAMES,output_path="output.mp4",    ))

To use our model with ComfyUI, please follow the instructions athttps://github.com/Lightricks/ComfyUI-LTXVideo/.

To use our model with the Diffusers Python library, check out theofficial documentation.

Diffusers also support an 8-bit version of LTX-Video,see details below

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
• Seeexamples for more inspiration.

When usingLTXVideoPipeline directly, you can enable prompt enhancement by settingenhance_prompt=True.

• Resolution Preset: Higher resolutions for detailed scenes, lower for faster generation and simpler scenes. The model works on resolutions that are divisible by 32 and number of frames that are divisible by 8 + 1 (e.g. 257). In case the resolution or number of frames are not divisible by 32 or 8 + 1, the input will be padded with -1 and then cropped to the desired resolution and number of frames. The model works best on resolutions under 720 x 1280 and number of frames below 257
• Seed: Save seed values to recreate specific styles or compositions you like
• Guidance Scale: 3-3.5 are the recommended values
• Inference Steps: More steps (40+) for quality, fewer steps (20-30) for speed

📝 For advanced parameters usage, please seepython inference.py --help

A community project providing additional nodes for enhanced control over the LTX Video model. It includes implementations of advanced techniques like RF-Inversion, RF-Edit, FlowEdit, and more. These nodes enable workflows such as Image and Video to Video (I+V2V), enhanced sampling via Spatiotemporal Skip Guidance (STG), and interpolation with precise frame settings.

• Repository:ComfyUI-LTXTricks
• Features:
  • 🔄RF-Inversion: ImplementsRF-Inversion with anexample workflow here.
  • ✂️RF-Edit: ImplementsRF-Solver-Edit with anexample workflow here.
  • 🌊FlowEdit: ImplementsFlowEdit with anexample workflow here.
  • 🎥I+V2V: Enables Video to Video with a reference image.Example workflow.
  • ✨Enhance: Partial implementation ofSTGuidance.Example workflow.
  • 🖼️Interpolation and Frame Setting: Nodes for precise control of latents per frame.Example workflow.

LTX-VideoQ8 is an 8-bit optimized version ofLTX-Video, designed for faster performance on NVIDIA ADA GPUs.

• Repository:LTX-VideoQ8
• Features:
  • 🚀 Up to 3X speed-up with no accuracy loss
  • 🎥 Generate 720x480x121 videos in under a minute on RTX 4060 (8GB VRAM)
  • 🛠️ Fine-tune 2B transformer models with precalculated latents
• Community Discussion:Reddit Thread
• Diffusers integration: A diffusers integration for the 8-bit model is already out!Details here

TeaCache is a training-free caching approach that leverages timestep differences across model outputs to accelerate LTX-Video inference by up to 2x without significant visual quality degradation.

• Repository:TeaCache4LTX-Video
• Features:
  • 🚀 Speeds up LTX-Video inference.
  • 📊 Adjustable trade-offs between speed (up to 2x) and visual quality using configurable parameters.
  • 🛠️ No retraining required: Works directly with existing models.

...is welcome! If you have a project or tool that integrates with LTX-Video,please let us know by opening an issue or pull request.

We provide an open-source repository for fine-tuning the LTX-Video model:LTX-Video-Trainer.This repository supports both the 2B and 13B model variants, enabling full fine-tuning as well as LoRA (Low-Rank Adaptation) fine-tuning for more efficient training. This includes:

• Control LoRAs: Train custom control models like depth, pose, and canny control
• Effect LoRAs: Create specialized effects and transformations for video generation

Explore the repository to customize the model for your specific use cases!More information and training instructions can be found in theREADME.

ComfyUI-LTXVideo repository now contains workflows and models for 3 specialized models that enable precise control over LTX-Video generation:

Pose Control, Depth Control and Canny Control

Example ComfyUI Workflow (for all control types):ic-lora.json

Want to work on cutting-edge AI research and make a real impact on millions of users worldwide?

AtLightricks, an AI-first company, we're revolutionizing how visual content is created.

If you are passionate about AI, computer vision, and video generation, we would love to hear from you!

Please visit ourcareers page for more information.

We are grateful for the following awesome projects when implementing LTX-Video:

• DiT andPixArt-alpha: vision transformers for image generation.

📄 Our tech report is out! If you find our work helpful, please ⭐️ star the repository and cite our paper.

@article{HaCohen2024LTXVideo,  title={LTX-Video: Realtime Video Latent Diffusion},  author={HaCohen, Yoav and Chiprut, Nisan and Brazowski, Benny and Shalem, Daniel and Moshe, Dudu and Richardson, Eitan and Levin, Eran and Shiran, Guy and Zabari, Nir and Gordon, Ori and Panet, Poriya and Weissbuch, Sapir and Kulikov, Victor and Bitterman, Yaki and Melumian, Zeev and Bibi, Ofir},  journal={arXiv preprint arXiv:2501.00103},  year={2024}}