Instructions to use inclusionAI/Ming-Lite-Uni with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use inclusionAI/Ming-Lite-Uni 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-Lite-Uni", 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:
- 4f05db5316ecd445ce3b5ae280035038b9398afc764d1b97e1a0891f0a57dd2a
- Size of remote file:
- 6.42 GB
- SHA256:
- 53053fe1b5f29b5a5a612717fee5b77dac5b3594989c7deae17c7cad45d4fd52
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