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
Explicit `text-to-video` pipeline tag and `diffusers` library
#72
by Wauplin HF Staff - opened
This PR explicitly sets text-to-video as pipeline_tag instead of getting it inferred by the server. It will help with discoverability + enable the text-to-video inference widget on the model page.
Note that the model is still tagged as both text-to-video and image-to-video so nothing will break in term of usage.
cc @sbrandeis
(should work as a replacement of #53)
Should we take this opportunity to add a library_name too?
Wauplin changed pull request title from Explicit `text-to-video` pipeline tag to Explicit `text-to-video` pipeline tag and `diffusers` library
Should we take this opportunity to add a library_name too?
Done!
yoavhacohen changed pull request status to merged