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 text_encoder/pytorch_model.fp16.bin from timbrooks/instruct-pix2pix: direct link, hf CLI and curl.
- Browser
- Download file 246 MB
-
https://huggingface.co/timbrooks/instruct-pix2pix/resolve/main/text_encoder/pytorch_model.fp16.bin
- Command line
-
hf download hf://timbrooks/instruct-pix2pix/text_encoder/pytorch_model.fp16.bin
-
curl -L -o pytorch_model.fp16.bin https://huggingface.co/timbrooks/instruct-pix2pix/resolve/main/text_encoder/pytorch_model.fp16.bin
246 MB
- Xet hash:
- d46f1f18c42a77832d73244cfd40c33237dbaf1009c6652d5180219f20f12165
- Size of remote file:
- 246 MB
- SHA256:
- 05eee911f195625deeab86f0b22b115d7d8bc3adbfc1404f03557f7e4e6a8fd7
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