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", torch_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
File size: 367 Bytes
9aff0cd | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | # 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
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