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Joypop
/
GDPO

Image-to-Image
Diffusers
Safetensors
super-resolution
image-restoration
one-step-generation
Model card Files Files and versions
xet
Community
1

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
GDPO / ram /models /__pycache__
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  • 2 contributors
History: 1 commit
Joypop's picture
Joypop
Add model weights
c3e16bb verified about 2 months ago
  • __init__.cpython-310.pyc
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  • ram.cpython-310.pyc
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  • ram_condition.cpython-310.pyc
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  • ram_lora.cpython-310.pyc
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  • swin_transformer.cpython-310.pyc
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  • utils.cpython-310.pyc
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  • vit.cpython-310.pyc
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