Instructions to use Rand000mGuy/bouncywalklow with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Rand000mGuy/bouncywalklow with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ostris/wan22_i2v_14b_orbit_shot_lora", torch_dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("Rand000mGuy/bouncywalklow") prompt = "Screenshot" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- e28abe68cd4139b8d975d8a34b1c2329e900e361553da89565e46c0830fb0d81
- Size of remote file:
- 307 MB
- SHA256:
- f9a1850ca0845261cb1ae97e4ec88044e6b68e3699496c3584cad1af3acecbf9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.