Instructions to use jay-jnp/F-ViTA_KAIST with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use jay-jnp/F-ViTA_KAIST 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("jay-jnp/F-ViTA_KAIST", 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:
- bcfb5cf03589825449979c743f6864981a80584dea9bf52acb5bf12f36ddc1a7
- Size of remote file:
- 6.88 GB
- SHA256:
- 572dd06169a63ca059269262731e89795c4a061dc34ac658f6968447ab721d25
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