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
| CUDA_VISIBLE_DEVICES=0, python GDPOSR/inferences/test.py \ | |
| --input_path test_LR \ | |
| --output_path experiment/GDPOSR \ | |
| --pretrained_path ckp/GDPOSR \ | |
| --pretrained_model_name_or_path stable-diffusion-2-1-base \ | |
| --ram_ft_path ckp/DAPE.pth \ | |
| --negprompt 'dotted, noise, blur, lowres, smooth' \ | |
| --prompt 'clean, high-resolution, 8k' \ | |
| --upscale 1 \ | |
| --time_step=100 \ | |
| --time_step_noise=250 |