Instructions to use hustvl/Moebius with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use hustvl/Moebius with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("hustvl/Moebius", 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 model card, pipeline tag, and links to paper/code
#1
by nielsr HF Staff - opened
This PR improves the model card for Moebius, adding:
- The
pipeline_tag: image-to-imageto the metadata for better discoverability. - Links to the paper (Moebius: 0.2B Lightweight Image Inpainting Framework with 10B-Level Performance), project page, and GitHub repository.
- Detailed instructions for environment setup, checkpoint organization, and running inference.
- BibTeX citation.