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
Automated Testing
Running tests locally
Additional requirements for running tests:
pip install pytest
pip install websocket-client==1.6.1
opencv-python==4.6.0.66
scikit-image==0.21.0
Run inference tests:
pytest tests/inference
Quality regression test
Compares images in 2 directories to ensure they are the same
- Run an inference test to save a directory of "ground truth" images
pytest tests/inference --output_dir tests/inference/baseline
Make code edits
Run inference and quality comparison tests
pytest