Instructions to use abcdgebop/moimad with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use abcdgebop/moimad with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("abcdgebop/moimad", 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:
- 6c02656015b86a0b282504e6875cf8fc8bbc172dd08e295d191c747a3cf2fd5d
- Size of remote file:
- 246 MB
- SHA256:
- b8adcc0e6e75cc72b72a1c223ecce32400a5aa4ceb0f0f7985f47060c3ba0841
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.