Hugging Face's logo Hugging Face
  • Models
  • Datasets
  • Spaces
  • Buckets new
  • Docs
  • Enterprise
  • Pricing
    • Website
      • Tasks
      • HuggingChat
      • Collections
      • Languages
      • Organizations
    • Community
      • Blog
      • Posts
      • Daily Papers
      • Learn
      • Discord
      • Forum
      • GitHub
    • Solutions
      • Team & Enterprise
      • Hugging Face PRO
      • Enterprise Support
      • Inference Providers
      • Inference Endpoints
      • Storage Buckets

  • Log In
  • Sign Up

Docty
/
dreambooth-chipped-coating-lora

Text-to-Image
Diffusers
TensorBoard
lora
diffusers-training
stable-diffusion
stable-diffusion-diffusers
Model card Files Files and versions
xet
Metrics Training metrics Community

Instructions to use Docty/dreambooth-chipped-coating-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Diffusers

    How to use Docty/dreambooth-chipped-coating-lora with Diffusers:

    pip install -U diffusers transformers accelerate
    import torch
    from diffusers import DiffusionPipeline
    
    # switch to "mps" for apple devices
    pipe = DiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", dtype=torch.bfloat16, device_map="cuda")
    pipe.load_lora_weights("Docty/dreambooth-chipped-coating-lora")
    
    prompt = "Create an object container whose entire surface is coated with a color but a has a rough wear off coating, tear off coating or chipped off coating or peeled off coating  exposing the bare uncovered metallic uncoated, sksks chip peeling, rough texture,  rusted or corroded surfaces, and signs of heavy wear, realistic light to show the  corroded surface, cinematic view, realistic"
    image = pipe(prompt).images[0]
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • Draw Things
  • DiffusionBee
dreambooth-chipped-coating-lora
38.7 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 3 commits
Docty's picture
Docty
End of training
20a0e82 verified about 1 year ago
  • checkpoint-10000
    End of training about 1 year ago
  • checkpoint-5000
    End of training about 1 year ago
  • logs
    End of training about 1 year ago
  • .gitattributes
    1.71 kB
    End of training about 1 year ago
  • README.md
    2.05 kB
    End of training about 1 year ago
  • image_0.png
    381 kB
    xet
    End of training about 1 year ago
  • image_1.png
    385 kB
    xet
    End of training about 1 year ago
  • image_2.png
    378 kB
    xet
    End of training about 1 year ago
  • image_3.png
    375 kB
    xet
    End of training about 1 year ago
  • pytorch_lora_weights.safetensors
    3.23 MB
    xet
    End of training about 1 year ago