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lightricks/ltx-video

10,252PythonAudience · developerComplexity · 4/5LicenseSetup · hard

TLDR

LTX-Video is an open-source video generation model by Lightricks that produces short video clips from text prompts or images, designed to run locally on your own GPU hardware.

Mindmap

mindmap
  root((repo))
    What it does
      Text to video
      Image to video
      Video extension
      Keyframe fill
    Tech Stack
      Python PyTorch
      CUDA GPU required
      Hugging Face weights
      ComfyUI integration
    Use Cases
      Content creation
      Concept drafts
      Research experiments
    Audience
      AI creators
      Researchers
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Things people build with this

USE CASE 1

Generate a short video clip from a text description to use as a concept draft or social media asset

USE CASE 2

Animate a still photograph by running image-to-video generation to add realistic motion

USE CASE 3

Fill in footage between two keyframe images to create smooth transitions

USE CASE 4

Extend an existing video clip forward or backward in time using the video continuation mode

Tech stack

PythonPyTorchCUDAHugging FaceComfyUI

Getting it running

Difficulty · hard Time to first run · 1h+

Requires a CUDA-capable GPU with significant VRAM and downloads large model weights from Hugging Face before first use.

Licensed under a custom Lightricks license with restrictions, check the repository for commercial and redistribution terms before using in a product.

In plain English

LTX-Video is an open-source video generation system built by Lightricks, the company behind the Facetune and LightLeap apps. At its core, it is a machine-learning model that takes a text description or an image and produces a short video clip. You type something like "a cat walking through autumn leaves" and the model renders it as moving footage. The system supports several generation modes. Text-to-video takes a written prompt and creates video from scratch. Image-to-video takes an existing photo and animates it, adding motion that fits the scene. You can also provide multiple keyframes, letting the model fill in the footage between two images you supply. There is a video extension mode that takes an existing clip and continues it forward or backward in time. Model sizes range from a 2-billion-parameter version, which runs on less powerful hardware, up to a 13-billion-parameter version aimed at higher quality results. Both come in distilled variants, which are faster to run, generating a preview in a few seconds on professional-grade hardware. The README describes output up to 4K resolution at up to 50 frames per second. A newer model called LTX-2 adds synchronized audio generation alongside the video, though LTX-2 lives in a separate repository. For people who want to run it locally, installation uses Python and pip, and the model weights are downloaded from Hugging Face. There is also integration with ComfyUI, a popular node-based visual tool that many AI image and video creators already use, which means you can slot LTX-Video into existing workflows without writing code. A set of control models for depth, pose, and edge detection let you constrain how the generated video looks, for example keeping a specific body pose across frames. The project is licensed under a custom Lightricks license visible in the repository. It is aimed at developers, researchers, and technically experienced creators who want to run video generation on their own machines or build it into their own tools.

Copy-paste prompts

Prompt 1
I installed LTX-Video and want to generate a 5-second clip of ocean waves at sunset. Write the Python command or ComfyUI workflow to do this with the 2-billion-parameter model.
Prompt 2
How do I use LTX-Video's image-to-video mode to animate a portrait photo so the subject appears to be breathing?
Prompt 3
I want to run the smaller 2-billion-parameter LTX-Video model instead of the 13-billion one to save VRAM. How do I select it and what is the minimum GPU memory I need?
Prompt 4
Show me how to use the depth control model in LTX-Video to keep the camera angle consistent across all frames of a generated clip.
Prompt 5
How do I add LTX-Video as a node in my existing ComfyUI workflow alongside Stable Diffusion image generation?
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