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quantum-whisper
/
edl-relight

Image-to-Video
Diffusers
Safetensors
video-diffusion
relighting
stable-diffusion
Model card Files Files and versions
xet
Community

Instructions to use quantum-whisper/edl-relight with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Diffusers

    How to use quantum-whisper/edl-relight with Diffusers:

    pip install -U diffusers transformers accelerate
    import torch
    from diffusers import DiffusionPipeline
    from diffusers.utils import load_image, export_to_video
    
    # switch to "mps" for apple devices
    pipe = DiffusionPipeline.from_pretrained("quantum-whisper/edl-relight", dtype=torch.bfloat16, device_map="cuda")
    pipe.to("cuda")
    
    prompt = "A man with short gray hair plays a red electric guitar."
    image = load_image(
        "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/guitar-man.png"
    )
    
    output = pipe(image=image, prompt=prompt).frames[0]
    export_to_video(output, "output.mp4")
  • Notebooks
  • Google Colab
  • Kaggle
edl-relight
9.28 GB
Ctrl+K
Ctrl+K
  • 2 contributors
History: 5 commits
jayhsu0627
minor
d4ff430 about 1 year ago
  • unet
    add json about 1 year ago
  • .gitattributes
    1.52 kB
    initial commit about 1 year ago
  • README.md
    472 Bytes
    minor about 1 year ago
  • optimizer.bin
    3.18 GB
    xet
    init model about 1 year ago
  • random_states_0.pkl
    15.1 kB
    xet
    init model about 1 year ago
  • scaler.pt
    988 Bytes
    xet
    init model about 1 year ago
  • scheduler.bin
    1 kB
    xet
    init model about 1 year ago