Instructions to use geyongtao/gvm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use geyongtao/gvm with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("geyongtao/gvm", 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
Add comprehensive model card for Generative Video Matting
#1
by nielsr HF Staff - opened
This PR adds a comprehensive model card for the Generative Video Matting model.
It includes:
- Relevant metadata (
license,library_name,pipeline_tag) to improve discoverability on the Hugging Face Hub (e.g., via https://huggingface.co/models?pipeline_tag=image-segmentation). - A link to the paper (Generative Video Matting), project page, and GitHub repository.
- The paper abstract for a quick overview of the model.
- Detailed environment setup, model download, and a command-line inference example for easy sample usage.
- The academic citation information.
Please review and merge if everything looks good.