Instructions to use yilingwang/sd-vae-ft-ema with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use yilingwang/sd-vae-ft-ema with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("yilingwang/sd-vae-ft-ema", 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:
- 190e5403de86291a750c27b3c73672933cc76a57aacdcd89878d57c33e356093
- Size of remote file:
- 335 MB
- SHA256:
- 7c98ebcd7ca5cb69d47b2ae287feba0695689fbf2c8fead2fab05fd3e0c28303
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