Instructions to use TaoLiveAIGC/AptAvatar with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use TaoLiveAIGC/AptAvatar with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("TaoLiveAIGC/AptAvatar", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Add license, pipeline tag
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by nielsr HF Staff - opened
README.md
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<div align="center">
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# AptAvatar: Fast and Vivid Long-Form Audio-Driven Video Generation for Production-Ready Avatars
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- [DMD](https://github.com/tianweiy/DMD2) and [Self-Forcing++](https://github.com/justincui03/Self-Forcing-Plus-Plus): key references for distribution matching distillation and self-forcing-style training.
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> [!TIP]
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> If you find AptAvatar useful, please also consider starring the original repositories of these foundational methods.
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license: apache-2.0
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pipeline_tag: image-to-video
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<div align="center">
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# AptAvatar: Fast and Vivid Long-Form Audio-Driven Video Generation for Production-Ready Avatars
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- [DMD](https://github.com/tianweiy/DMD2) and [Self-Forcing++](https://github.com/justincui03/Self-Forcing-Plus-Plus): key references for distribution matching distillation and self-forcing-style training.
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> [!TIP]
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> If you find AptAvatar useful, please also consider starring the original repositories of these foundational methods.
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