Instructions to use GasaiYU/PAM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use GasaiYU/PAM with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("GasaiYU/PAM", 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:
- 2ef6e64ec68b85f226b9abd61a6461ece1d931f73ead4bd0ace12b3ca6b9506b
- Size of remote file:
- 2.98 GB
- SHA256:
- 2798ff4946918c2648b2f5f7dfd2c82d0f3cb98fe970078f7f57c13e26858da8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.