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
Commit History
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Add model card, pipeline tag, and links to paper/code
Verified signatureThis PR improves the model card for **Moebius**, adding: - The `pipeline_tag: image-to-image` to the metadata for better discoverability. - Links to the paper ([Moebius: 0.2B Lightweight Image Inpainting Framework with 10B-Level Performance](https://huggingface.co/papers/2606.19195)), project page, and GitHub repository. - Detailed instructions for environment setup, checkpoint organization, and running inference. - BibTeX citation.db5c76d
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