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:
- e225bf942a01b6cf52caed7f32325d3c6f79b9d02fd76af2f9d867908dfa4bc1
- Size of remote file:
- 5 GB
- SHA256:
- 3ae31e62a829309f9c21435a64d5760c5d45e734050905d6e266d061621c48cb
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