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:
- 37e30876bbb64945b94113cb3f32e2d7b1f6659136a1eba4b4050031ac338bd6
- Size of remote file:
- 5 GB
- SHA256:
- 2dd0a1b768a6d29acc80052c0dfaa5d24358b465e5d0e78a46aad0c46965c075
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