Image-to-Video
Diffusers
ONNX
Safetensors
WanPipeline
video-generation
video diffusion transformer
audio-driven avatar animation
Instructions to use FrancisRing/StableAvatar with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use FrancisRing/StableAvatar 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("FrancisRing/StableAvatar", 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
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<span>Comparison results between StableAvatar and state-of-the-art (SOTA) audio-driven avatar video generation models highlight the superior performance of StableAvatar in delivering <b>infinite-length, high-fidelity, identity-preserving avatar animation</b>.</span>
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<video src="https://github.com/user-attachments/assets/90691318-311e-40b9-9bd9-62db83ab1492" width="768" controls loop></video>
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<span>Comparison results between StableAvatar and state-of-the-art (SOTA) audio-driven avatar video generation models highlight the superior performance of StableAvatar in delivering <b>infinite-length, high-fidelity, identity-preserving avatar animation</b>.</span>
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