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bytedance-research
/
UMO

Text-to-Image
Diffusers
English
image-generation
subject-personalization
Diffusion-Transformer
Model card Files Files and versions
xet
Community
1

Instructions to use bytedance-research/UMO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Diffusers

    How to use bytedance-research/UMO with Diffusers:

    pip install -U diffusers transformers accelerate
    import torch
    from diffusers import DiffusionPipeline
    
    # switch to "mps" for apple devices
    pipe = DiffusionPipeline.from_pretrained("bytedance-research/UMO", dtype=torch.bfloat16, device_map="cuda")
    
    prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
    image = pipe(prompt).images[0]
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • Draw Things
  • DiffusionBee
UMO
2.57 GB
Ctrl+K
Ctrl+K
  • 2 contributors
History: 11 commits
cb1cyf's picture
cb1cyf
Update README.md
3ae6b3a verified 8 months ago
  • assets
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  • .gitattributes
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  • README.md
    8.28 kB
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  • UMO_OmniGen2.safetensors
    654 MB
    xet
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  • UMO_UNO.safetensors
    1.91 GB
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  • config.json
    66 Bytes
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