Instructions to use jdopensource/JoyAI-Image-Edit-Diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use jdopensource/JoyAI-Image-Edit-Diffusers 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("jdopensource/JoyAI-Image-Edit-Diffusers", 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:
- a5086d6ede3c25a13f4e68849c7323a7ba284ee6e7d1ac46fd33ef7fe7fe3879
- Size of remote file:
- 2.07 MB
- SHA256:
- 231d190581919ab3e4105500bbe29f4dd4d0a6cf8d6c58f0bc46500e45e407db
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.