Instructions to use Lightricks/LTX-Video with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Lightricks/LTX-Video with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image, export_to_video # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Lightricks/LTX-Video", dtype=torch.bfloat16, device_map="cuda") pipe.to("cuda") prompt = "A man with short gray hair plays a red electric guitar." image = load_image( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/guitar-man.png" ) output = pipe(image=image, prompt=prompt).frames[0] export_to_video(output, "output.mp4") - Notebooks
- Google Colab
- Kaggle
LTX-Video β the most efficient video gen for mobile?
#121
by 3morixd - opened
Lightricks has been quietly building the most efficient video generation models. LTX-Video is designed for real-time, which makes it the most promising for mobile.
If quantized to ~1.5GB, LTX could potentially generate short clips on Snapdragon 865. We're watching closely.
Potential collab: mobile-optimized LTX for MENA market?
- Dispatch AI (FZE), Sharjah UAE