Instructions to use Runware/Kandinsky-5.0-I2I-Lite-sft-Diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Runware/Kandinsky-5.0-I2I-Lite-sft-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("Runware/Kandinsky-5.0-I2I-Lite-sft-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:
- 844bb4cee77a5bea2928cfbf87836ac7d5fee3f69cd2b793e303dec762bdf9dd
- Size of remote file:
- 1.71 GB
- SHA256:
- a2bf730a0c7debf160f7a6b50b3aaf3703e7e88ac73de7a314903141db026dcb
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.