Instructions to use kandinskylab/Kandinsky-5.0-I2I-Lite-pretrain-Diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use kandinskylab/Kandinsky-5.0-I2I-Lite-pretrain-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("kandinskylab/Kandinsky-5.0-I2I-Lite-pretrain-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:
- 292b8d452b4aca3fc79e999af98c7b5b026eacac1a144a5d3722f9d86428c177
- Size of remote file:
- 1.71 GB
- SHA256:
- 156f677ed4495acd1ec7197249c091b85c240267c82f2f7f2e4eae4177931fed
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