Instructions to use aa-studio/aa_studio_data with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use aa-studio/aa_studio_data with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("aa-studio/aa_studio_data", 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:
- a3bdf047a39eee88267e9741d371ed0dc095521059a5019cf0e17e615c41799e
- Size of remote file:
- 5.71 GB
- SHA256:
- 36b26b559614076434165a74ef19e7652bd582d36ccc315cbd1ca4b10a7e7d55
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.