Instructions to use timbrooks/instruct-pix2pix with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use timbrooks/instruct-pix2pix 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("timbrooks/instruct-pix2pix", 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
Download vae/diffusion_pytorch_model.bin from timbrooks/instruct-pix2pix: direct link, hf CLI and curl.
- Browser
- Download file 335 MB
-
https://huggingface.co/timbrooks/instruct-pix2pix/resolve/refs%2Fpr%2F1/vae/diffusion_pytorch_model.bin
- Command line
-
hf download hf://timbrooks/instruct-pix2pix@refs/pr/1/vae/diffusion_pytorch_model.bin
-
curl -L -o diffusion_pytorch_model.bin https://huggingface.co/timbrooks/instruct-pix2pix/resolve/refs%2Fpr%2F1/vae/diffusion_pytorch_model.bin
335 MB
- Xet hash:
- 16b3d5d6b914117425b7a5bf913b7e61ff9444e6e3f2bf8e3aaee21fb1328fa5
- Size of remote file:
- 335 MB
- SHA256:
- af27ea858349760ebe3311953e0bfe8d6fd257dc9537ae0b2b938c262132a2c6
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