Instructions to use Tinsae/soju with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Tinsae/soju with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Tinsae/soju", 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:
- 80dbd64ba7e17b41218fb00e1ff45212d5a36bb32a18009eff1fbfa626184be0
- Size of remote file:
- 2.13 GB
- SHA256:
- 02938def2c553af492887966417426cdb47069bf4abdebf45a7c51e1dfafa1c1
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