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: 579 Bytes
7609008 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | {
"_class_name": "EdenLivePipeline",
"_edenlive_original": {},
"transformer": ["diffusers", "EdenLiveTransformer3DModel"],
"vae": ["diffusers", "AutoencoderKLWan"],
"decoder": ["diffusers", "EdenLiveLightTAEDecoder"],
"text_encoder": ["transformers", "UMT5EncoderModel"],
"tokenizer": ["transformers", "AutoTokenizer"],
"proxy_encoder": ["transformers", "DINOv3ViTModel"],
"proxy_projection": ["diffusers", "EdenLiveProxyProjection"],
"scheduler": ["diffusers", "UniPCMultistepScheduler"],
"image_encoder": [null, null],
"image_processor": [null, null]
}
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