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
File size: 514 Bytes
de40b28 | 1 2 3 4 5 6 7 8 9 | # NOTICE — vision-encoder
The weights in this directory (`backbone/` and `projection.safetensors`) are DINOv3 by
Meta Platforms, Inc. or derivative works of it made by Vast AI. They are distributed under
the DINOv3 License (see `LICENSE.md` here and in `backbone/`), not under the Apache 2.0
license that covers the rest of this repository. Use must comply with that license,
including its trade-control and prohibited-use terms. Research publications that use these
weights must acknowledge the use of DINOv3.
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