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", torch_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:
- 868f77a0e66ad6387ad199b20a4b7b145944dad2919d96d3d4768a36199ab950
- Size of remote file:
- 681 MB
- SHA256:
- 47308e18effe65596d08e6faf820afb00698e23c4934b7fc8699af6b354c1a40
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