Instructions to use babkasotona/vae with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use babkasotona/vae with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("babkasotona/vae", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- df4b64a34c32b302c0e7eca8a4c576e5876693110c6900b82593b32a070fcce1
- Size of remote file:
- 336 MB
- SHA256:
- 6369e370ff02168a240a9ebfd47810dd7babb36f76b7d9999e5d78cb4a1976c2
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