Instructions to use stdstu123/LynnReal-Onmi-light-vae with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use stdstu123/LynnReal-Onmi-light-vae with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("stdstu123/LynnReal-Onmi-light-vae", 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
Download NOTICE from stdstu123/LynnReal-Onmi-light-vae: direct link, hf CLI and curl.
- Browser
- Download file 378 Bytes
-
https://huggingface.co/stdstu123/LynnReal-Onmi-light-vae/resolve/main/NOTICE
- Command line
-
hf download hf://stdstu123/LynnReal-Onmi-light-vae/NOTICE
-
curl -L -o NOTICE https://huggingface.co/stdstu123/LynnReal-Onmi-light-vae/resolve/main/NOTICE
378 Bytes
| MiniMax H3 is licensed under the MiniMax H3 Community License Agreement, Copyright © 2026 MiniMax. All Rights Reserved. | |
| LynnReal-Omni provides modified model weights and inference adaptations. The H3 weights, Qwen conditioner, Diffusers implementation and bundled FBGEMM kernels retain their original licenses and notices. See the model cards and bundled dependency licenses. | |