Instructions to use tsync-co/sd-vae-ft-mse-original with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use tsync-co/sd-vae-ft-mse-original with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("tsync-co/sd-vae-ft-mse-original", 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:
- 0aa89c245774808867e0835cd47966c8cd1aaf36dc99ace2df137e19da549d37
- Size of remote file:
- 335 MB
- SHA256:
- c6a580b13a5bc05a5e16e4dbb80608ff2ec251a162311590c1f34c013d7f3dab
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