Instructions to use Muapi/hold-up-dance with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Muapi/hold-up-dance with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import export_to_video # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("wan-ai/Wan2.1-T2V-14B-Diffusers", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("Muapi/hold-up-dance") prompt = "A man with short gray hair plays a red electric guitar." output = pipe(prompt=prompt).frames[0] export_to_video(output, "output.mp4") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- Xet hash:
- 0202c87f400fbb256790187f9e40a17836843a16c7e06153a53ea1842f4d379d
- Size of remote file:
- 10.6 MB
- SHA256:
- 4f3f091ef497415db3a4edba4176ec68f96bb02e793e73144378017d09e2b2bc
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