Instructions to use chenzeyang1/T with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use chenzeyang1/T with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("chenzeyang1/T", 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

- Xet hash:
- 31b6181ec25761d59ca39eb2c8b13e191244e11c5ba1c84bb078da376a018e9a
- Size of remote file:
- 339 kB
- SHA256:
- 80c62bc1e38f39c34b17525445307fbc1ea6fe689f96b5c87fdcdfbffea9f071
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