Instructions to use ShambaC/SAR-Intruct-Pix2Pix with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ShambaC/SAR-Intruct-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("ShambaC/SAR-Intruct-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
- Xet hash:
- 9841cea3d7c4effcd99a89931b5d72f389df386af8821860b0c92089fe09fd87
- Size of remote file:
- 6.88 GB
- SHA256:
- 602706036e7d556e874592c0d10ee76b4024864b2b77a21b1af33a540e5fc3ef
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.