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

BiliSakura
/
RS-Painter-Diffusers

Image-to-Image
Diffusers
Safetensors
StableDiffusionInpaintPipeline
stable-diffusion
remote-sensing
semantic-segmentation
diffusion-models
few-shot
sat-imagery
StableDiffusionInpaintPipeline
Model card Files Files and versions
xet
Community

Instructions to use BiliSakura/RS-Painter-Diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Diffusers

    How to use BiliSakura/RS-Painter-Diffusers with Diffusers:

    pip install -U diffusers transformers accelerate
    import torch
    from diffusers import AutoPipelineForInpainting
    from diffusers.utils import load_image
    
    # switch to "mps" for apple devices
    pipe = AutoPipelineForInpainting.from_pretrained("BiliSakura/RS-Painter-Diffusers", dtype=torch.float16, variant="fp16", device_map="cuda")
    
    img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
    mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"
    
    image = load_image(img_url).resize((1024, 1024))
    mask_image = load_image(mask_url).resize((1024, 1024))
    
    prompt = "a tiger sitting on a park bench"
    generator = torch.Generator(device="cuda").manual_seed(0)
    
    image = pipe(
      prompt=prompt,
      image=image,
      mask_image=mask_image,
      guidance_scale=8.0,
      num_inference_steps=20,  # steps between 15 and 30 work well for us
      strength=0.99,  # make sure to use `strength` below 1.0
      generator=generator,
    ).images[0]
  • Notebooks
  • Google Colab
  • Kaggle
RS-Painter-Diffusers
4.27 GB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 17 commits
BiliSakura's picture
BiliSakura
Update README.md
6aa97d2 verified 4 months ago
  • feature_extractor
    Upload RS-Painter model files 5 months ago
  • scheduler
    Upload RS-Painter model files 5 months ago
  • text_encoder
    Upload RS-Painter model files 5 months ago
  • tokenizer
    Upload RS-Painter model files 5 months ago
  • unet
    Upload RS-Painter model files 5 months ago
  • vae
    Upload RS-Painter model files 5 months ago
  • .gitattributes
    1.52 kB
    initial commit 5 months ago
  • README.md
    2.63 kB
    Update README.md 4 months ago
  • model_index.json
    410 Bytes
    Upload RS-Painter model files 5 months ago