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
      • Hardware
      • Learn
      • Discord
      • Forum
      • GitHub
    • Solutions
      • Team & Enterprise
      • Hugging Face PRO
      • Enterprise Support
      • Inference Providers
      • Inference Endpoints
      • Storage Buckets

  • Log In
  • Sign Up

PricedAsh
/
eggplant-model

Text-to-Image
Diffusers
Safetensors
StableDiffusionPipeline
Model card Files Files and versions
xet
Community
1

Instructions to use PricedAsh/eggplant-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Diffusers

    How to use PricedAsh/eggplant-model with Diffusers:

    pip install -U diffusers transformers accelerate
    import torch
    from diffusers import DiffusionPipeline
    
    # switch to "mps" for apple devices
    pipe = DiffusionPipeline.from_pretrained("PricedAsh/eggplant-model", 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 Settings
  • Draw Things
  • DiffusionBee
eggplant-model
63.1 GB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 15 commits
PricedAsh's picture
PricedAsh
6cf4c4b0e937f3da65042a62169929cd19d109168d0f5b74318f49c137644c3e
cd10ef5 almost 3 years ago
  • checkpoint-1000
    d2f4b5b5eb0c5b58b55e22a0bdaa191336c2cafd11d79488779710c653bcd2c3 almost 3 years ago
  • checkpoint-1500
    e617eeefb02e0f2abed51fee76cdf581c6b2c03598ca27ab6473c532d1a1cb57 almost 3 years ago
  • checkpoint-2000
    05deffff6f576c832546c07003de238a3dc25f9716d743aae5ace9064e4f8709 almost 3 years ago
  • checkpoint-500
    1bc89d85636ed55bc2ff5b0464f8d50a81270401e36a744ceab9d63599726fa8 almost 3 years ago
  • feature_extractor
    1bc89d85636ed55bc2ff5b0464f8d50a81270401e36a744ceab9d63599726fa8 almost 3 years ago
  • scheduler
    1bc89d85636ed55bc2ff5b0464f8d50a81270401e36a744ceab9d63599726fa8 almost 3 years ago
  • text_encoder
    6cf4c4b0e937f3da65042a62169929cd19d109168d0f5b74318f49c137644c3e almost 3 years ago
  • tokenizer
    6cf4c4b0e937f3da65042a62169929cd19d109168d0f5b74318f49c137644c3e almost 3 years ago
  • unet
    6cf4c4b0e937f3da65042a62169929cd19d109168d0f5b74318f49c137644c3e almost 3 years ago
  • vae
    6cf4c4b0e937f3da65042a62169929cd19d109168d0f5b74318f49c137644c3e almost 3 years ago
  • .gitattributes
    1.52 kB
    initial commit almost 3 years ago
  • model_index.json
    598 Bytes
    1bc89d85636ed55bc2ff5b0464f8d50a81270401e36a744ceab9d63599726fa8 almost 3 years ago