Instructions to use Joypop/GDPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Joypop/GDPO 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("Joypop/GDPO", 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
| from torchvision.transforms import Normalize, Compose, Resize, ToTensor | |
| def convert_to_rgb(image): | |
| return image.convert("RGB") | |
| def get_transform(image_size=384): | |
| return Compose([ | |
| convert_to_rgb, | |
| Resize((image_size, image_size)), | |
| ToTensor(), | |
| Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) | |
| ]) | |