Instructions to use ngoctham/SwiftVR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ngoctham/SwiftVR with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ngoctham/SwiftVR", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1bc7f8c3d55ece59326e9dfd50af8b48b9931bbc65f27c0f8f0df34835561584
- Size of remote file:
- 60.5 MB
- SHA256:
- 696127bbaf6f04382bdcf3889d84d5c6ebf9d933d8bac01d2c659a9e2027ef3e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.