Instructions to use Insta360-Research/DiT360-Panorama-Image-Generation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Insta360-Research/DiT360-Panorama-Image-Generation with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Insta360-Research/DiT360-Panorama-Image-Generation", 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
- Local Apps Settings
- Draw Things
- DiffusionBee
Enhance model card: Add pipeline tag, library name, and abstract
#1
by nielsr HF Staff - opened
This PR improves the model card for DiT360 by adding:
pipeline_tag: text-to-imageto enhance discoverability for users looking for text-to-image generation models.library_name: diffusersto indicate compatibility with thediffuserslibrary, enabling the automated "How to use" widget on the model page.- The paper's abstract, providing a concise summary of the research directly in the model card. The GitHub repository link within the abstract has also been clarified.
These changes will make the model more informative and accessible to the Hugging Face community.
Thanks a lot for your contribution! Really appreciate your help and the time you took to improve the project.
Insta360-Research changed pull request status to merged