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Veda-Labs
/
Amazing-2.0

Image-to-Image
Diffusers
Safetensors
Diffusion Single File
English
VedikaAmazing2Pipeline
image-generation
image-editing
vedika-amazing-2
Model card Files Files and versions
xet
Community
1

Instructions to use Veda-Labs/Amazing-2.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Diffusers

    How to use Veda-Labs/Amazing-2.0 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("Veda-Labs/Amazing-2.0", 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]
  • Diffusion Single File

    How to use Veda-Labs/Amazing-2.0 with Diffusion Single File:

    # No code snippets available yet for this library.
    
    # To use this model, check the repository files and the library's documentation.
    
    # Want to help? PRs adding snippets are welcome at:
    # https://github.com/huggingface/huggingface.js
  • Notebooks
  • Google Colab
  • Kaggle
New discussion
Resources
  • PR & discussions documentation
  • Code of Conduct
  • Hub documentation

Could you please create quantized versions of this model that could fit under 24GB VRAM

1
#1 opened 1 day ago by
parthwagh
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