Instructions to use epfl-vita/svi-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use epfl-vita/svi-model 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("epfl-vita/svi-model", 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
Improve model card: Add pipeline tag, library name, license, and update paper/project links
#6
by nielsr HF Staff - opened
This PR enhances the model card for Stable Video Infinity by:
- Adding
pipeline_tag: image-to-videoto categorize the model correctly and improve discoverability on the Hugging Face Hub. - Including
library_name: diffusersto enable the automated "How to use" widget, as the model is compatible with the Diffusers library. - Adding the
license: mittag. - Updating the paper link in the YAML metadata and the associated badge to the official Hugging Face Papers page: Stable Video Infinity: Infinite-Length Video Generation with Error Recycling.
- Adding the
project_pagemetadata entry and a dedicated badge for the project page: https://stable-video-infinity.github.io/homepage/. - Adding the Hugging Face paper URL to the bibtex citation.
These updates provide a more comprehensive and user-friendly experience for those exploring this model.
vita-video-gen changed pull request status to merged
Thank you so much for the PR!
Best,
SVI Team