Instructions to use Muapi/djz-inline-skater with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Muapi/djz-inline-skater 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/djz-inline-skater") 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:
- 1a3f20f012a60c166652d8d344eabd3c7d8714af73eced2d34081616a85b2e9a
- Size of remote file:
- 2.65 MB
- SHA256:
- 0dda45bc951962a0a9a51fd82893bfd317b954ba88bacfa3d9a7bb1d87de1d4b
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