Instructions to use SsharvienKumar/SWoMo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use SsharvienKumar/SWoMo 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("SsharvienKumar/SWoMo", 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
Improve model card metadata and links
#1
by nielsr HF Staff - opened
Hi! I'm Niels from the community science team at Hugging Face.
I've updated the model card for SWoMo to include:
- Metadata for the
image-to-videopipeline tag. library_name: diffusersbased on the configuration files mentioningdiffusersclasses and versions.- Links to the paper, GitHub repository, and project page for better accessibility.
This will help users find and use your model more effectively on the Hugging Face Hub!
SsharvienKumar changed pull request status to merged
Thanks for the update