Instructions to use drozbay/HuMoveLora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use drozbay/HuMoveLora with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("bytedance-research/HuMo", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("drozbay/HuMoveLora") prompt = "-" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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README.md
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<Gallery />
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## Model description
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<video controls width="640" src="./images/WanMoveLoraTesting_00036-audio.mp4"></video>
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A LoRA that tries to combine track controlled motion of [Wan-Move](https://github.com/ali-vilab/Wan-Move) with the human motion and speech sync features of [HuMo](https://huggingface.co/bytedance-research/HuMo).
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## Usage
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<Gallery />
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## Model description
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<video controls width="640" src="https://huggingface.co/drozbay/HuMoveLora/resolve/main/images/WanMoveLoraTesting_00036-audio.mp4"></video>
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A LoRA that tries to combine track controlled motion of [Wan-Move](https://github.com/ali-vilab/Wan-Move) with the human motion and speech sync features of [HuMo](https://huggingface.co/bytedance-research/HuMo).
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## Usage
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