- Introduction
- What's new
- Models
- Quick Start Guide
- Model User Guide
- Community Contribution
- Training
- Control Models
- Join Us!
- Acknowledgement
LTX-Video is the first DiT-based video generation model that can generate high-quality videos in real-time. It can generate 30 FPS videos at 1216×704 resolution, faster than it takes to watch them. 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, keyframe-based animation, video extension (both forward and backward), video-to-video transformations, and any combination of these features.
- 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 in ComfyUI.
- Release a new distilled models:
- 13B distilled model ltxv-13b-0.9.8-distilled
- 2B distilled model ltxv-2b-0.9.8-distilled
- Both models are distilled from the same base model ltxv-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 model LTX-Video-ICLoRA-detailer-13B-0.9.8
- Available in ComfyUI.
- 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 model ltxv-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 model ltxv-13b-0.9.7-distilled-fp8 for real-time generation (on H100) with even less VRAM
- Release a new 13B model ltxv-13b-0.9.7-dev
- Release a new quantized model ltxv-13b-0.9.7-dev-fp8 for faster inference with less VRam
- Release a new upscalers
- Breakthrough prompt adherence and physical understanding.
- New Pipeline for multi-scale video rendering for fast and high quality results
- Release a new checkpoint ltxv-2b-0.9.6-dev-04-25 with improved quality
- Release a new distilled model ltxv-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 in LTX-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
- Add ComfyUI-LTXTricks
- Add Diffusion-Pipe
- Release the research 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
| Name | Notes | inference.py config | ComfyUI workflow (Recommended) |
|---|---|---|---|
| ltxv-13b-0.9.8-dev | Highest quality, requires more VRAM | ltxv-13b-0.9.8-dev.yaml | ltxv-13b-i2v-base.json |
| ltxv-13b-0.9.8-mix | Mix ltxv-13b-dev and ltxv-13b-distilled in the same multi-scale rendering workflow for balanced speed-quality | N/A | ltxv-13b-i2v-mixed-multiscale.json |
| ltxv-13b-0.9.8-distilled | Faster, less VRAM usage, slight quality reduction compared to 13b. Ideal for rapid iterations | ltxv-13b-0.9.8-distilled.yaml | ltxv-13b-dist-i2v-base.json |
| ltxv-2b-0.9.8-distilled | Smaller model, slight quality reduction compared to 13b distilled. Ideal for fast generation with light VRAM usage | ltxv-2b-0.9.8-distilled.yaml | N/A |
| ltxv-13b-0.9.8-dev-fp8 | Quantized version of ltxv-13b | ltxv-13b-0.9.8-dev-fp8.yaml | ltxv-13b-i2v-base-fp8.json |
| ltxv-13b-0.9.8-distilled-fp8 | Quantized version of ltxv-13b-distilled | ltxv-13b-0.9.8-distilled-fp8.yaml | ltxv-13b-dist-i2v-base-fp8.json |
| ltxv-2b-0.9.8-distilled-fp8 | Quantized version of ltxv-2b-distilled | ltxv-2b-0.9.8-distilled-fp8.yaml | N/A |
| ltxv-2b-0.9.6 | Good quality, lower VRAM requirement than ltxv-13b | ltxv-2b-0.9.6-dev.yaml | ltxvideo-i2v.json |
| ltxv-2b-0.9.6-distilled | 15× faster, real-time capable, fewer steps needed, no STG/CFG required | ltxv-2b-0.9.6-distilled.yaml | ltxvideo-i2v-distilled.json |
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.git
cd LTX-Video
# create env
python -m venv env
source env/bin/activate
python -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 our ComfyUI 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 in inference.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.yamlYou 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.yamlfrom ltx_video.inference import infer, InferenceConfig
infer(
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 at https://github.com/Lightricks/ComfyUI-LTXVideo/.
To use our model with the Diffusers Python library, check out the official 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
- See examples for more inspiration.
When using LTXVideoPipeline directly, you can enable prompt enhancement by setting enhance_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 see python 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: Implements RF-Inversion with an example workflow here.
- ✂️ RF-Edit: Implements RF-Solver-Edit with an example workflow here.
- 🌊 FlowEdit: Implements FlowEdit with an example workflow here.
- 🎥 I+V2V: Enables Video to Video with a reference image. Example workflow.
- ✨ Enhance: Partial implementation of STGuidance. Example workflow.
- 🖼️ Interpolation and Frame Setting: Nodes for precise control of latents per frame. Example workflow.
LTX-VideoQ8 is an 8-bit optimized version of LTX-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 the README.
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?
At Lightricks, 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 our careers page for more information.
We are grateful for the following awesome projects when implementing LTX-Video:
- DiT and PixArt-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}
}