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
Video Patterns
#4
by Scofano - opened
First, congratulations. It is the best local video model so far.
Is there a way to avoid (smooth?) those video patterns (like a grid over the video)?
These are artifacts, most likely from the perceptual loss used during training.
We are trying to reduce them with better training schemes.
From some attempts we made, you can use enhancers/upsamplers that are generative themselves to get rid of the artifact, but you pay in inconsistency.