Instructions to use babkasotona/vae7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use babkasotona/vae7 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("babkasotona/vae7", 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:
- 5b96ecba78f15d8eea204cdb37e9cfbf8f58dfb7266d7dd4ffef5a6609536198
- Size of remote file:
- 383 MB
- SHA256:
- 3cb2ab0cd4b85bfb59a4f7e0741f1dc266df1f3811b138ab213cb3c08fe136f6
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