Instructions to use VAST-AI/EdenLive with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use VAST-AI/EdenLive 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("VAST-AI/EdenLive", 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
Download vision-encoder/backbone/configuration.json from VAST-AI/EdenLive: direct link, hf CLI and curl.
- Browser
- Download file 74 Bytes
-
https://huggingface.co/VAST-AI/EdenLive/resolve/main/vision-encoder/backbone/configuration.json
- Command line
-
hf download hf://VAST-AI/EdenLive/vision-encoder/backbone/configuration.json
-
curl -L -o configuration.json https://huggingface.co/VAST-AI/EdenLive/resolve/main/vision-encoder/backbone/configuration.json
74 Bytes
| {"framework": "pytorch", "task": "text-generatiion", "allow_remote": true} |