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
| diffusers==0.25.0 | |
| torch==2.0.1 | |
| transformers==4.28.1 | |
| xformers==0.0.20 | |
| einops==0.7.0 | |
| open-clip-torch==2.20.0 | |
| peft==0.9.0 | |
| Pillow==9.5.0 | |
| PyYAML==6.0 | |
| huggingface_hub==0.25.2 | |
| numpy==1.23.5 | |
| loralib | |
| basicsr | |
| fairscale |