Instructions to use CiroN2022/mpred with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use CiroN2022/mpred with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("CiroN2022/mpred", 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:
- 5f0e58a39ebe37bae3393797105d5a2986ee714566c8b6d3d5f99b1a7eec1c57
- Size of remote file:
- 2.13 GB
- SHA256:
- cd4fb54564e666f9a82309fa70b20575a2d6a38fe523d71d2f93d9323bae7259
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