Instructions to use cuongdev/3nu-3000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use cuongdev/3nu-3000 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("cuongdev/3nu-3000", 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:
- 991f51c579c606fbcd5372efde7bde67a5b0384f81bdde6022828d972073fdac
- Size of remote file:
- 3.46 GB
- SHA256:
- bc13fc7b4ccc512042b55749d9adba7f34e170e76fe170cf24c2e2b56a46bd4b
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