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:
- 2074f39d567c3c54097759d9eb71239e942239fb001138c1d8c9b993e811c4a9
- Size of remote file:
- 5 GB
- SHA256:
- 4c5f3eba2d9ff17f97806d1344a978d42aee271fd34f9502d6afc28419734c80
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