Instructions to use benjamin-paine/vidxtend with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use benjamin-paine/vidxtend with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("benjamin-paine/vidxtend", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Improve model card: Add pipeline tag, library name, link to code, and correct license
#2
by nielsr HF Staff - opened
This PR improves the model card by:
- Adding the
pipeline_tagto enable discovery via the text-to-video pipeline. - Adding the
library_nameto facilitate one-click inference. - Adding a link to the official code repository.
- Correcting the license to
mitbased on the Github repository.
benjamin-paine changed pull request status to merged