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McGill-NLP
/
AURORA

Image-to-Image
Diffusers
Safetensors
English
StableDiffusionInstructPix2PixPipeline
editing
vision-language
Model card Files Files and versions
xet
Community
1

Instructions to use McGill-NLP/AURORA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Diffusers

    How to use McGill-NLP/AURORA 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("McGill-NLP/AURORA", 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
AURORA
5.48 GB
Ctrl+K
Ctrl+K
  • 2 contributors
History: 7 commits
BennoKrojer's picture
BennoKrojer
ludolara's picture
ludolara
Update README.md (#1)
26c4fc0 verified over 1 year ago
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