Instructions to use Arrexel/pattern-diffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Arrexel/pattern-diffusion with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Arrexel/pattern-diffusion", 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
Great job
#1
by omarei - opened
So cool. Is this a new arch or built on top of another?
It's built on top of stable-diffusion-2-base. Repeating patterns need a far smaller model than a general-purpose diffusion model and it was a good tradeoff between size and inference speed.
Arrexel changed discussion status to closed