Image-to-Image
Diffusers
Safetensors
StableDiffusionInpaintPipeline
stable-diffusion
remote-sensing
semantic-segmentation
diffusion-models
few-shot
sat-imagery
StableDiffusionInpaintPipeline
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", torch_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
Upload RS-Painter model files
Browse files
feature_extractor/preprocessor_config.json
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{
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"crop_size": 224,
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"do_center_crop": true,
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"do_convert_rgb": true,
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"do_normalize": true,
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"do_resize": true,
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"feature_extractor_type": "CLIPFeatureExtractor",
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"image_mean": [
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0.48145466,
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0.4578275,
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0.40821073
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],
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"image_std": [
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0.26862954,
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0.26130258,
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0.27577711
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],
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"resample": 3,
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"size": 224
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}
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