Instructions to use GD-ML/Omni-Effects with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use GD-ML/Omni-Effects with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("GD-ML/Omni-Effects", 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
Download pipeline.jpg from GD-ML/Omni-Effects: direct link, hf CLI and curl.
- Browser
- Download file 1.47 MB
-
https://huggingface.co/GD-ML/Omni-Effects/resolve/main/pipeline.jpg
- Command line
-
hf download hf://GD-ML/Omni-Effects/pipeline.jpg
-
curl -L -o pipeline.jpg https://huggingface.co/GD-ML/Omni-Effects/resolve/main/pipeline.jpg
1.47 MB

- Xet hash:
- c1c0c0cab37d30961f429b96bc6a79e3398ea5f35cde9899dafb29f295df2c48
- Size of remote file:
- 1.47 MB
- SHA256:
- f5aec0b65e1788fb3307eab8814706921859bb107dadce41b7d66bf867197f0e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.