Instructions to use babkasotona/vae8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use babkasotona/vae8 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/vae8", 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:
- eaeafb748da4f82792875bc22756100de70d456dd7c75c157b16d06b322e1f86
- Size of remote file:
- 383 MB
- SHA256:
- d0963803789980cdf3df4d4e87b26c099ccd3ed08b78deb7a12afaba5e193c97
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