Image-to-Video
Diffusers
Safetensors
English
modular-diffusers
world-model
video-generation
audio-visual
camera-control
echo-wm
Instructions to use Echo-Team/Echo-WM-Base-Diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Echo-Team/Echo-WM-Base-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("Echo-Team/Echo-WM-Base-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
- Xet hash:
- 23db2e055d80cccf03598ded35b611c37cce36e1bc0bf93b87209e2a480a073d
- Size of remote file:
- 33.4 MB
- SHA256:
- daab2354f8a74e70d70b4d1f804939b68a8c9624dd06cb7858e52dd8970e9726
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