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 quantitative.png from GD-ML/Omni-Effects: direct link, hf CLI and curl.
- Browser
- Download file 534 kB
-
https://huggingface.co/GD-ML/Omni-Effects/resolve/main/quantitative.png
- Command line
-
hf download hf://GD-ML/Omni-Effects/quantitative.png
-
curl -L -o quantitative.png https://huggingface.co/GD-ML/Omni-Effects/resolve/main/quantitative.png
534 kB

- Xet hash:
- be03734ed322cefb528f9edd21f0aef0c141f391ac7b8a810fde9e2cf05d08b8
- Size of remote file:
- 534 kB
- SHA256:
- e8d846c900549d8d4c1ab1ad4d1c7ff5b2d9e0a9e6a6886543580625bfb2b808
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