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:
- 5265579dfd0a55dfe9c620b29496eeec9cc530d8259207d7c5ecd265e5ebc726
- Size of remote file:
- 335 MB
- SHA256:
- 37eb3e09ae1ce3d6891ddf809ca927b618e501091142cf07fdd9cd170e3a046f
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