Instructions to use Kunbyte/ROSE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Kunbyte/ROSE with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Kunbyte/ROSE", 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
Improve model card: Add pipeline tag, library name, and enrich content with abstract, usage, and results
#1
by nielsr HF Staff - opened
This PR enhances the model card for the ROSE model by:
- Adding
pipeline_tag: video-to-videoto improve discoverability on the Hub. - Adding
library_name: diffusers, as the model is built on Diffusion Transformers and usesdiffuserscomponents, enabling automated usage snippets. - Updating the paper link to the official Hugging Face paper page: https://huggingface.co/papers/2508.18633.
- Including the paper's abstract to provide a comprehensive overview of the model's capabilities and methodology.
- Adding explicit links to the project page, GitHub repository, and Hugging Face Space for easy access.
- Incorporating detailed "Dependencies and Installation" and "Usage (Quick Test)" sections with code snippets directly from the official GitHub README.
- Adding a "Results" section with visual examples and an "Overview" diagram from the GitHub README (with updated image links to raw GitHub URLs).
- Including the "Citation" and "Acknowledgement" sections for proper attribution.
These changes significantly improve the model card's completeness and user-friendliness.
weiyuchoumou526 changed pull request status to merged