Instructions to use VAST-AI/EdenLive-14B-Diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use VAST-AI/EdenLive-14B-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-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
Download image_encoder/config.json from VAST-AI/EdenLive-14B-Diffusers: direct link, hf CLI and curl.
- Browser
- Download file 498 Bytes
-
https://huggingface.co/VAST-AI/EdenLive-14B-Diffusers/resolve/main/image_encoder/config.json
- Command line
-
hf download hf://VAST-AI/EdenLive-14B-Diffusers/image_encoder/config.json
-
curl -L -o config.json https://huggingface.co/VAST-AI/EdenLive-14B-Diffusers/resolve/main/image_encoder/config.json
498 Bytes
| { | |
| "architectures": [ | |
| "CLIPVisionModel" | |
| ], | |
| "attention_dropout": 0.0, | |
| "dropout": 0.0, | |
| "dtype": "bfloat16", | |
| "hidden_act": "gelu", | |
| "hidden_size": 1280, | |
| "image_size": 224, | |
| "initializer_factor": 1.0, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 5120, | |
| "layer_norm_eps": 1e-05, | |
| "model_type": "clip_vision_model", | |
| "num_attention_heads": 16, | |
| "num_channels": 3, | |
| "num_hidden_layers": 32, | |
| "patch_size": 14, | |
| "projection_dim": 1024, | |
| "transformers_version": "5.12.1" | |
| } | |