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
LynnReal Lightweight Video VAE
Code: LynnReal-AI/LynnReal-Omni
A complete codec with the unchanged MiniMax H3 encoder and a distilled 26-block video decoder. Latent channels, normalization, spatial scale and temporal compression match the official VAE. Deployment uses the decoder EMA parameters.
config.json and the indexed Safetensors shards follow the Diffusers component
layout. decode_config.json records the release decoder settings. Load with
model.light_vae.LightVAE.from_pretrained("weight/light-vae"), or pass
--light-vae weight/light-vae to script/sample.py. Native decoding tiles are
the default; precision and tile geometry must accompany speed/quality comparisons.
The codec changes reconstruction, not the denoiser latent interface. The same
latent need not decode to identical RGB under the official and lightweight VAE.
See LICENSE and the release NOTICE for MiniMax H3 terms and attribution.
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