Instructions to use rcannizzaro/vae-dsprites with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use rcannizzaro/vae-dsprites with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("rcannizzaro/vae-dsprites", 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:
- 3c0979f21acde0f439cd278d31acbda5378e8fde24a4a93d53792681d0850326
- Size of remote file:
- 335 MB
- SHA256:
- 124e03ac01b9dc178e97f0ef8bd78539194858856567294e860259c0e778063e
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