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:
- 9bea7e5fe166bbc48278215d38be3ef7c58c205eee51394c8c304cb5ee46fe3d
- Size of remote file:
- 335 MB
- SHA256:
- 91be4f4d8ae5e73ab2cf04c95cc850a5e49affb92deb7a8c766cbb04b096a54f
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