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
| # 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 | |
| 1) Run an inference test to save a directory of "ground truth" images | |
| ``` | |
| pytest tests/inference --output_dir tests/inference/baseline | |
| ``` | |
| 2) Make code edits | |
| 3) Run inference and quality comparison tests | |
| ``` | |
| pytest | |
| ``` |