Instructions to use ostris/vae-kl-f8-d16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ostris/vae-kl-f8-d16 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ostris/vae-kl-f8-d16", 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
Add SD1.5 example to README.MD
#3
by shachargluska - opened
Oh wow! I was just putting that adapter there for testing for now, I didn’t expect anyone to find it or figure out how to use it yet, but you implemented it perfectly! The adapter is not quite where I want it yet, but it is where people can start messing with it I guess. Thank you so much for this and the time you put into figuring it all out with zero documentation.
ostris changed pull request status to merged
