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
Download checkpoint-15000/optimizer.bin from ShambaC/SAR-Intruct-Pix2Pix: direct link, hf CLI and curl.
- Browser
- Download file 6.88 GB
-
https://huggingface.co/ShambaC/SAR-Intruct-Pix2Pix/resolve/main/checkpoint-15000/optimizer.bin
- Command line
-
hf download hf://ShambaC/SAR-Intruct-Pix2Pix/checkpoint-15000/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/ShambaC/SAR-Intruct-Pix2Pix/resolve/main/checkpoint-15000/optimizer.bin
6.88 GB
- Xet hash:
- 1e4476a33a6c0bd4be23b97a8e5a90b171b90dafe0ffba9e4f77d7f62baf1539
- Size of remote file:
- 6.88 GB
- SHA256:
- 349fb1b2a138d9e6729e1ef2e703cd3ce42b38730c8db907f2e79fcac2bf7632
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.