Instructions to use lavinal712/transfusion-vae with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use lavinal712/transfusion-vae with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("lavinal712/transfusion-vae", 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:
- 910f2008828a0d8cb44e86d78dfd93ad2758f22c6eb4407d79ad1ff6b9cec46b
- Size of remote file:
- 335 MB
- SHA256:
- f80f4fb80d56c1f509503fc3d3a7edee51d2e1330fadec26680eed9fc6ac5383
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