Instructions to use MIN-Lab/minWM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use MIN-Lab/minWM 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("MIN-Lab/minWM", 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
Add model card and metadata for Causal Forcing
#1
by nielsr HF Staff - opened
This PR improves the model card for Causal Forcing. It adds the relevant image-to-video pipeline tag for better discoverability and includes links to the research paper, project page, and official GitHub repository. It also provides sample inference commands for both chunk-wise and frame-wise models based on the official implementation.
zhuhz22 changed pull request status to merged