Instructions to use milaidy/bootoshi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use milaidy/bootoshi with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("milaidy/bootoshi", 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
- Draw Things
- DiffusionBee
- Xet hash:
- c5c55467c48fc00b9aec416e9ea0bebd35beb633c88ffc18a18175cf28f23044
- Size of remote file:
- 3.44 GB
- SHA256:
- a0517a5c831141a55517f4f9d7a9d916b3a047b21e2f3abd255a2f39a286b0de
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