Instructions to use Muapi/beej with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Muapi/beej 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("Lightricks/LTX-Video", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("Muapi/beej") 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:
- e7f10dac3ab56518c67d06ae9fc7e8c752af81e8de9d069270f7c3f982ff1063
- Size of remote file:
- 1.23 GB
- SHA256:
- a3e5f348bdb47ab7731fd8bd4499fe60d27a26202a508f79c9468a24412880c0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.