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:
- 7dacc8d0ff5b602a42b32f2630e5b92248d7d82f923760f38c7fcc5bfeed0893
- Size of remote file:
- 246 MB
- SHA256:
- 31ea93228d1f23b34b12bec8333965675bee529dd70ae96f7df7059d621dc2ef
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