Instructions to use KevinHuang/OmniX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use KevinHuang/OmniX with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("KevinHuang/OmniX", 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: Update pipeline tag, add library_name, extend tags, and import full GitHub README content
#1
by nielsr HF Staff - opened
This PR significantly improves the model card for OmniX by:
- Updating the
pipeline_tagtotext-to-3dto accurately reflect its core capability of generating graphics-ready 3D scenes. - Adding
library_name: diffusersas the model's codebase is built on the Diffusers library, as evidenced by the acknowledgements in the GitHub README. - Extending
tagswithtext-to-imageandimage-to-imageto cover the model's panorama generation and perception capabilities from text and image prompts. - Incorporating a comprehensive overview, installation instructions, detailed inference examples with code snippets, and visual demonstrations directly from the project's GitHub README to provide a rich and informative model card.
- Ensuring direct links to the paper (arXiv), project page, and GitHub repository are prominently displayed using badges.
KevinHuang changed pull request status to merged