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
File size: 382 Bytes
c3e16bb e518cbe c3e16bb | 1 2 3 4 5 6 7 8 9 10 11 | 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 |