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:
- 4dc9f69ca952c833115a5e40ee602ef4b1dd461f27a5af0eaf6e0b816015e6c8
- Size of remote file:
- 5 GB
- SHA256:
- af57712f08cb240c7246f8f9257bbbeff3ae91ba7252a4292cd3785a10ad9101
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