Instructions to use suimu/VIRES with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use suimu/VIRES 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("suimu/VIRES", 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
Add pipeline tag, library name and license
#1
by nielsr HF Staff - opened
This PR adds the pipeline_tag as metadata, ensuring the model can be found at https://huggingface.co/models?pipeline_tag=image-to-video. It also adds the library_name and license. It also improves the clarity of the model description and adds a more concise usage example.
suimu changed pull request status to merged