Instructions to use RyanHangZhou/PICS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use RyanHangZhou/PICS with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("RyanHangZhou/PICS", dtype=torch.bfloat16, device_map="cuda") prompt = "Turn this cat into a dog" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") image = pipe(image=input_image, prompt=prompt).images[0] - Notebooks
- Google Colab
- Kaggle
| # LVIS train set | |
| python -m datasets.lvis \ | |
| --dataset_dir "/path/to/raw_data" \ | |
| --construct_dataset_dir "data/train/LVIS" \ | |
| --area_ratio 0.02 \ | |
| --is_build_data \ | |
| --is_train | |
| # LVIS test set | |
| python -m datasets.lvis \ | |
| --dataset_dir "/path/to/raw_data" \ | |
| --construct_dataset_dir "data/test/LVIS" \ | |
| --area_ratio 0.02 \ | |
| --is_build_data | |