Instructions to use ktsintsis/trainedDataset with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ktsintsis/trainedDataset with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ktsintsis/trainedDataset", 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:
- 9e6e873598e9c6a3ddc9455e9baffee04ccb9da9684246acf52d883dfbd04c09
- Size of remote file:
- 455 MB
- SHA256:
- 96a729acfa4e88909d8afeccdc36261aefe7d92dd5e89525b413dd8981baedbf
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