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
- Xet hash:
- f50cf4023e9eccf3b9ae08c4a08b79c10b267ce44cfadf10b0aa89c1f5448fdc
- Size of remote file:
- 1.72 GB
- SHA256:
- 8ff9533fc8c3fc62c4013f0625e8ba5e83e1b84f7448ce638515b547419a7e1e
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