Instructions to use vipul319/try-on with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use vipul319/try-on 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("vipul319/try-on", 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:
- 68f2a8154af66d5420061227e8189488e1eee3d37bcc4dab824e210b56c5c534
- Size of remote file:
- 267 MB
- SHA256:
- 8436e1dae96e2601c373d1ace29c8f0978b16357d9038c17a8ba756cca376dbc
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