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:
- d2b6025977d08cfbf471e3b9adf484ef2892541cf5042adf5aab591765e4e907
- Size of remote file:
- 681 MB
- SHA256:
- 35462d08c78b81d178e4a863d6c7f237033f4314e5ee1102f9856bc2c6c2392c
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