Instructions to use diffusionbee/fooocus_inpainting with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use diffusionbee/fooocus_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("diffusionbee/fooocus_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:
- 612d7248c5a3f1d3907653bb9dc10147e1c5a1734ac17ef9f1a559296a647222
- Size of remote file:
- 1.39 GB
- SHA256:
- 4bc8195d6dda6c00ec2a911e708ce82e5183e02811af751de596a44ac27b8ace
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