Instructions to use cuongdev/3nu-2000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use cuongdev/3nu-2000 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-2000", 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:
- db2ea4608108de84c97d2988fa74915f26a8868c71df74c768f7a8776ac5bdef
- Size of remote file:
- 681 MB
- SHA256:
- dcf7297b4ab21e0e72d82daac052cf7cfb8ecc5a02225cc188be64ac84db71e9
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