Instructions to use ckpt/f222-inpainting with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ckpt/f222-inpainting 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("ckpt/f222-inpainting", 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:
- 97c94538bf09028c494a31dd205441cb649edcfcc493dbfc33b8fd50178512f7
- Size of remote file:
- 167 MB
- SHA256:
- 1f1cbb27d5d4e766854751c93a066caee68242a3a91531e4f62ce7715e19536a
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