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", torch_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:
- 7cd0a3d02078e9a34bc9b851bbb23ac4b8d64bf2cbc753fff774fc86b83fcdde
- Size of remote file:
- 2.13 GB
- SHA256:
- f2d45bbd0f7d28b7ff5e33cf5f7dca82ccf7a14fa55a867ec4dc7f701cb2859f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.