Instructions to use ahmedesmail16/Image-To-Image with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ahmedesmail16/Image-To-Image 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("ahmedesmail16/Image-To-Image", 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:
- 7d36af2d2cb5e13b3c84dc9345b2efe28f684139413bb634c7dac852645d9c1f
- Size of remote file:
- 335 MB
- SHA256:
- 716971093e3428c9156906fcbcc5500abf005317c5f4d3a5bb3fa28c45e1e071
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