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:
- a5c02db9e8331ef6c8ecf64c31655c448a222d83e4474842a8cb480e18d27f2f
- Size of remote file:
- 335 MB
- SHA256:
- 1500aa1c70ed177316fbfe0fbd97756096b32c9b9ff345fda96ca8a561d958a9
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