Instructions to use inclusionAI/Ming-flash-omni-Preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use inclusionAI/Ming-flash-omni-Preview with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("inclusionAI/Ming-flash-omni-Preview", torch_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:
- 81082c01c06b39a73b8b11896b8084f2bf15f3f789b8c9207c8a1282641c25b9
- Size of remote file:
- 5 GB
- SHA256:
- 83cc7644ced11a858cbc01e27fd7bda355ead651b974b3940f59bc0f1e872562
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.