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wassimm
/
SDXL

Text-to-Image
Diffusers
Trained with AutoTrain
Model card Files Files and versions
xet
Community

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

  • Libraries
  • Diffusers

    How to use wassimm/SDXL with Diffusers:

    pip install -U diffusers transformers accelerate
    import torch
    from diffusers import DiffusionPipeline
    
    # switch to "mps" for apple devices
    pipe = DiffusionPipeline.from_pretrained("wassimm/SDXL", dtype=torch.bfloat16, device_map="cuda")
    
    prompt = "A captivating, existing living room updated with French style, keeping all current structural elements. Add warm, golden hues, plush velvet sofas, ornate gilded mirrors, and a crystal chandelier that casts enchanting reflections on the polished parquet floor. Ensure no alterations to structural elements"
    image = pipe(prompt).images[0]
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • Draw Things
  • DiffusionBee
SDXL
23.4 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 4 commits
wassimm's picture
wassimm
Rename model_index.json to training_params.json
7437bab verified almost 2 years ago
  • .gitattributes
    1.52 kB
    initial commit almost 2 years ago
  • README.md
    517 Bytes
    End of training almost 2 years ago
  • prompt.txt
    304 Bytes
    End of training almost 2 years ago
  • pytorch_lora_weights.safetensors
    23.4 MB
    xet
    End of training almost 2 years ago
  • training_params.json
    1.68 kB
    Rename model_index.json to training_params.json almost 2 years ago