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
Update scripts/test/test.sh
Browse files- scripts/test/test.sh +2 -2
scripts/test/test.sh
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CUDA_VISIBLE_DEVICES=0, python GDPOSR/inferences/test.py \
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--pretrained_path ckp/GDPOSR \
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--pretrained_model_name_or_path stable-diffusion-2-1-base \
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--ram_ft_path ckp/DAPE.pth \
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CUDA_VISIBLE_DEVICES=0, python GDPOSR/inferences/test.py \
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--input_path test_LR \
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--output_path experiment/GDPOSR \
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--pretrained_path ckp/GDPOSR \
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--pretrained_model_name_or_path stable-diffusion-2-1-base \
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--ram_ft_path ckp/DAPE.pth \
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