Instructions to use recoilme/vae8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use recoilme/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("recoilme/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:
- 1fa7878290ebcf8f7f0bce1d47084b1ca913395867270e18417858b1f0a9e763
- Size of remote file:
- 383 MB
- SHA256:
- b6342c154910ff1065c25915b107ab1326f146c44055f3a4119ffb95f5159a4f
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