Instructions to use Wan-AI/Wan2.2-S2V-14B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Wan-AI/Wan2.2-S2V-14B 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("Wan-AI/Wan2.2-S2V-14B", 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: Add pipeline tag, library name, specific paper, and project page for Wan-S2V
#5
by nielsr HF Staff - opened
This PR updates the model card for Wan2.2-S2V-14B by:
- Adding
library_name: diffusersto the metadata, as indicated by the model's configuration and its compatibility with thediffuserslibrary. - Adding
pipeline_tag: otherto the metadata to better classify its audio-driven video generation functionality. - Updating the main paper link in the introduction and navigation bar to the specific
Wan-S2V: Audio-Driven Cinematic Video Generationpaper (https://huggingface.co/papers/2508.18621). - Adding the abstract of the
Wan-S2Vpaper for improved context. - Adding an explicit link to the project page: https://humanaigc.github.io/wan-s2v-webpage.
These changes will enhance the model's discoverability on the Hugging Face Hub and provide more precise and comprehensive documentation for users.
kelseye changed pull request status to merged