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
Commit History
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Document optional Echo-WM action overlay
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Use encode_video for audiovisual export
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Use built-in Echo-WM Base sampling defaults
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Fix Modular Pipeline CPU offloading example
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Use the official Echo-WM Base example
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Fix Diffusers component repository references
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Upload README.md with huggingface_hub
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Upload folder using huggingface_hub
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initial commit
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