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 decoder-5b/lighttaew2_2.pth from VAST-AI/EdenLive: direct link, hf CLI and curl.
- Browser
- Download file 45.7 MB
-
https://huggingface.co/VAST-AI/EdenLive/resolve/main/decoder-5b/lighttaew2_2.pth
- Command line
-
hf download hf://VAST-AI/EdenLive/decoder-5b/lighttaew2_2.pth
-
curl -L -o lighttaew2_2.pth https://huggingface.co/VAST-AI/EdenLive/resolve/main/decoder-5b/lighttaew2_2.pth
45.7 MB
- Xet hash:
- 9f8e4086ec2972b91ccb9d911030581f83edaae5a6a675e95dcbbebccc8135bf
- Size of remote file:
- 45.7 MB
- SHA256:
- e62630fe8cf2a8a3ba1b23e0361e87d8128eed79fbf46e6c4287e33b7be2bd82
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