Instructions to use Liangyingping/L2M-Inpainting with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Liangyingping/L2M-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("Liangyingping/L2M-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:
- c65985264715807b9c1a2ac4b3630b835d975ddfbf7328595d4c2d7f796c3cda
- Size of remote file:
- 1.36 GB
- SHA256:
- 67e013543d4fac905c882e2993d86a2d454ee69dc9e8f37c0c23d33a48959d15
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