Instructions to use cuongdev/nmthu-22 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use cuongdev/nmthu-22 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/nmthu-22", 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:
- 1667e71ee76233decb7cb8896a1b18a425c74506f7339d7258a6c59c7181e527
- Size of remote file:
- 3.46 GB
- SHA256:
- fd8a233642e455277155a5623ead839df15cf51161b7a66454abb358ad900230
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