Instructions to use udg/omx3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use udg/omx3 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("udg/omx3", 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
- Xet hash:
- c4ed32121e87e35374b5bfa136bf31b09913096a5d80f63593aba1104792e7f2
- Size of remote file:
- 492 MB
- SHA256:
- c4cc18312db7db7d3147ca87929089f8bd332eacf0786416493e7f0a5f173134
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