Instructions to use babkasotona/vae2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use babkasotona/vae2 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/vae2", 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:
- 15ec39abb1e6068d3012780e4e17152d67a858bf8c5cb943aad1c2947e9fd4c2
- Size of remote file:
- 383 MB
- SHA256:
- 767cde6350cdb14ae4404fc7869dda15eb2045be2c2e06fe81d1b0a1126528e1
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