Image-to-Image
Diffusers
StableDiffusionInpaintPipeline
stable-diffusion
stable-diffusion-diffusers
text-guided-to-image-inpainting
endpoints-template
Instructions to use philschmid/stable-diffusion-2-inpainting-endpoint with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use philschmid/stable-diffusion-2-inpainting-endpoint with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("philschmid/stable-diffusion-2-inpainting-endpoint", dtype=torch.bfloat16, device_map="cuda") prompt = "Turn this cat into a dog" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") image = pipe(image=input_image, prompt=prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6e96810f24962433636c9e85b2d125c025cd1199f98f3214d99962db72abf531
- Size of remote file:
- 1.73 GB
- SHA256:
- 20722649ed12ff926183129d7f2f7957388d03edc79888be6da8e3346ef9e873
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