Instructions to use Drexubery/UniView with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Drexubery/UniView with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image, export_to_video # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Drexubery/UniView", dtype=torch.bfloat16, device_map="cuda") pipe.to("cuda") prompt = "A man with short gray hair plays a red electric guitar." image = load_image( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/guitar-man.png" ) output = pipe(image=image, prompt=prompt).frames[0] export_to_video(output, "output.mp4") - Notebooks
- Google Colab
- Kaggle
Demo for this model on Spaces
Hi @Drexubery π€
I'm Linoy from the open-source team at Hugging Face - congrats on UniWorld-View!
We built with an agent an interactive demo app for it on Hugging Face Spaces, running on free ZeroGPU infrastructure: https://huggingface.co/spaces/hugging-apps/uniworld-view-nvs
We would love to transfer this demo over to you or your organization - just let me know here which username or org to transfer to. We hope it can give your work more visibility, discoverability and let folks try it out.
In the future, feel free to already ship models with demos included. You can use this one as a blueprint to build by yourself or with the help of an agent - you can load the huggingface-spaces skill on Claude Code, Codex, Pi, etc.
Cheers,
Linoy
HI Linoy,
Thanks for your contribution! Please transfer to https://huggingface.co/Drexubery which is my huggingface account.
Cheers,
Wangbo