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edw15
/
kandinsky_base

Image-to-Image
Diffusers
Safetensors
KandinskyV22Img2ImgCombinedPipeline
Model card Files Files and versions
xet
Community

Instructions to use edw15/kandinsky_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Diffusers

    How to use edw15/kandinsky_base 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("edw15/kandinsky_base", torch_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

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  • movq
    Upload KandinskyV22Img2ImgCombinedPipeline almost 2 years ago
  • prior_image_encoder
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  • prior_image_processor
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  • prior_prior
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  • prior_scheduler
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  • prior_text_encoder
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  • prior_tokenizer
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  • scheduler
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  • unet
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  • .gitattributes
    1.52 kB
    initial commit almost 2 years ago
  • README.md
    5.16 kB
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  • config.json
    873 Bytes
    Create config.json almost 2 years ago
  • model_index.json
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