Instructions to use Skywork/Matrix-Game-2.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Skywork/Matrix-Game-2.0 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("Skywork/Matrix-Game-2.0", 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: update paper link, add abstract and BibTeX citation
#3
by nielsr HF Staff - opened
This PR improves the model card for Matrix-Game 2.0 by:
- Updating the paper link in the overview section to point to the official Hugging Face Papers page: https://huggingface.co/papers/2508.13009. This makes it easier for users to find and reference the academic paper directly from the Hugging Face Hub.
- Adding the full abstract of the paper, providing a quick summary of the model's capabilities and contributions.
- Populating the empty BibTeX citation section with the correct entry, which is crucial for academic attribution.
These changes enhance the discoverability and completeness of the model card.
crispeng changed pull request status to closed