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:
- c6695cb0fc5ddd0d65c0fbf4fd64f9b0f358301d9f364c6cc5d312b3d3413179
- Size of remote file:
- 2.61 GB
- SHA256:
- 22e99048718311ff63970c6d056513bb1b014503ac3695b0486109ea5380fffa
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