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
- Draw Things

- Xet hash:
- 936709022e102263b066f8cd4f948a02d008e3405f6e3ec2bf317a48b16b407e
- Size of remote file:
- 1.52 MB
- SHA256:
- c19fc510d9a3ff2357d65bef22be896090f0117a51d139fc7dc5e71f085c2f4e
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