Instructions to use RedRocket/furception_vae with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use RedRocket/furception_vae 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("RedRocket/furception_vae", 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:
- b207eab7f5c43275105ad61af47408d272a29853b336e8501fcd1d668fc5be14
- Size of remote file:
- 335 MB
- SHA256:
- 24e650297c4cb02ef81a02cf502aeb74a9b651eae4ce90bbbd315f0c101f45b0
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