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MAVS
/
urkov1

Text-to-Image
Diffusers
StableDiffusionPipeline
Model card Files Files and versions
xet
Community
4

Instructions to use MAVS/urkov1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Diffusers

    How to use MAVS/urkov1 with Diffusers:

    pip install -U diffusers transformers accelerate
    import torch
    from diffusers import DiffusionPipeline
    
    # switch to "mps" for apple devices
    pipe = DiffusionPipeline.from_pretrained("MAVS/urkov1", 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
urkov1
Ctrl+K
Ctrl+K
  • 2 contributors
History: 4 commits
patrickvonplaten's picture
patrickvonplaten
Add `clip_sample=False` to scheduler to make model compatible with DDIM.
57fa36b over 3 years ago
  • feature_extractor
    Upload diffusers weights over 3 years ago
  • safety_checker
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  • scheduler
    Add `clip_sample=False` to scheduler to make model compatible with DDIM. over 3 years ago
  • text_encoder
    Upload diffusers weights over 3 years ago
  • tokenizer
    Upload diffusers weights over 3 years ago
  • unet
    Upload diffusers weights over 3 years ago
  • vae
    Upload diffusers weights over 3 years ago
  • .gitattributes
    1.48 kB
    initial commit over 3 years ago
  • model_index.json
    552 Bytes
    Upload diffusers weights over 3 years ago
  • urko_dog.ckpt
    2.13 GB
    xet
    Upload urko_dog.ckpt over 3 years ago