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
File size: 563 Bytes
6f7e8eb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | # 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
``` |