Instructions to use VAST-AI/EdenLive-14B-Causal-Diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use VAST-AI/EdenLive-14B-Causal-Diffusers 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-14B-Causal-Diffusers", 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: 708 Bytes
97a8e1a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | # NOTICE — clip
The image encoder originates from LAION CLIP ViT-H/14 trained on LAION-2B
using OpenCLIP, by Romain Beaumont on Stability AI compute:
https://huggingface.co/laion/CLIP-ViT-H-14-laion2B-s32B-b79K
The upstream model card declares the MIT license. The original OpenCLIP
MIT permission and copyright notice are reproduced in LICENSE.md:
https://github.com/mlfoundations/open_clip/blob/main/LICENSE
Conversion into a Diffusers-compatible component does not remove the original
MIT attribution. EdenLive's Apache-2.0 code license does not replace the MIT
license applicable to this component. Processing and loading use the separately
licensed Hugging Face Transformers / Diffusers libraries.
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