Instructions to use andrewheins55/Ming-flash-omni-2.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use andrewheins55/Ming-flash-omni-2.1 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("andrewheins55/Ming-flash-omni-2.1", 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:
- 44ace5cfaf6ba1d087bde8ef30599648e66f80ba1492bd23eb044a4dd69915b1
- Size of remote file:
- 5 GB
- SHA256:
- 6553a3359a0b17e52c223080492a97d995bee7203f0ba59beb1876279a52de33
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.