Instructions to use acul3/aculstabble with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use acul3/aculstabble with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("acul3/aculstabble", 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
- Local Apps Settings
- Draw Things
- DiffusionBee
Commit History
-
Add `scale_factor` to vae config.
Hey munggok 👋, Your model repository seems to contain a stable diffusion checkpoint. We have just merged a [PR](https://github.com/huggingface/diffusers/pull/1860) in `diffusers` which makes `scaling_factor` (the magic 0.18215 number a config attribute of `vae`). We strongly recommend that you merge this PR to make sure your model keeps working correctly with diffusers. Diffusingly, Suraj.07286ce
-
init files
ef8d86e