Instructions to use cicada-ai/Jogg-Avatar-V2V-Infinite with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- InfiniteTalk
How to use cicada-ai/Jogg-Avatar-V2V-Infinite with InfiniteTalk:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
Jogg-Avatar V2V 1.3B (InfiniteTalk)
Jogg-Avatar V2V 1.3B is an audio-driven video-to-video avatar model based on Wan2.1-T2V-1.3B and the InfiniteTalk approach. It preserves source body, background, and camera motion while regenerating a face synchronized to a driving audio track.
This model card is prepared for the InfiniteTalk checkpoint release. Training, preprocessing, and inference code is available at chanjing-ai/Jogg-Avatar-V2V-1.3B.
The checkpoint directory is expected to contain:
Jogg-Avatar-V2V-1.3B/
|-- model-00001-of-00002.safetensors
|-- model-00002-of-00002.safetensors
|-- model.safetensors.index.json
`-- training_init/audio_proj.safetensors
Wan2.1-T2V-1.3B, TencentGameMate/chinese-wav2vec2-base, and the face preprocessing models are required separately. Review all upstream licenses and terms before use.
Users are responsible for obtaining consent for source videos and voices and for clearly disclosing synthetic media. Do not use this model for impersonation, fraud, harassment, or deceptive content.
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Model tree for cicada-ai/Jogg-Avatar-V2V-Infinite
Base model
Wan-AI/Wan2.1-T2V-1.3B